human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "approximating the exponential the lanczos method and an tilde o m time spectral algorithm for balanced separator"
  Abstract: We give a novel spectral approximation algorithm for the balanced separator problem that, given a graph G, a constant balance b \in (0,1/2], and a parameter \gamma, either finds an \Omega(b)-balanced cut of conductance O(\sqrt(\gamma)) in G, or outputs a certificate that all b-balanced cuts in G have conductance at least \gamma, and runs in time \tilde{O}(m). This settles the question of designing asymptotically optimal spectral algorithms for balanced separator. Our algorithm relies on a variant of the heat kernel random walk and requires, as a subroutine, an algorithm to compute \exp(-L)v where L is the Laplacian of a graph related to G and v is a vector. Algorithms for computing the matrix-exponential-vector product efficiently comprise our next set of results. Our main result here is a new algorithm which computes a good approximation to \exp(-A)v for a class of PSD matrices A and a given vector u, in time roughly \tilde{O}(m_A), where m_A is the number of non-zero entries of A. This uses, in a non-trivial way, the result of Spielman and Teng on inverting SDD matrices in \tilde{O}(m_A) time. Finally, we prove e^{-x} can be uniformly approximated up to a small additive error, in a non-negative interval [a,b] with a polynomial of degree roughly \sqrt{b-a}. While this result is of independent interest in approximation theory, we show that, via the Lanczos method from numerical analysis, it yields a simple algorithm to compute \exp(-A)v for PSD matrices that runs in time roughly O(t_A \sqrt{||A||}), where t_A is the time required for computation of the vector Aw for given vector w. As an application, we obtain a simple and practical algorithm, with output conductance O(\sqrt(\gamma)), for balanced separator that runs in time \tilde{O}(m/\sqrt(\gamma)). This latter algorithm matches the running time, but improves on the approximation guarantee of the algorithm by Andersen and Peres.

Connected through hyperedge HE-1 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond"):
  with members:
    - "faster p norm minimizing flows via smoothed q norm problems"
    - "fast provably convergent irls algorithm for p norm linear regression"
    - "distributed edge connectivity in sublinear time"
    - "improved distributed expander decomposition and nearly optimal triangle enumeration"
    - "flows in almost linear time via adaptive preconditioning"
    - "breaking quadratic time for small vertex connectivity and an approximation scheme"
    - "a new algorithm for decremental single source shortest paths with applications to vertex capacitated flow and cut problems"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"

Connected through hyperedge HE-2 (induced by source paper "flows in almost linear time via adaptive preconditioning"):
  with members:
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "expander decomposition and pruning faster stronger and simpler"
    - "solving directed laplacian systems in nearly linear time through sparse lu factorizations"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "much faster algorithms for matrix scaling"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "near optimal approximate shortest paths and transshipment in distributed and streaming models"
    - "asymptotic behavior of ell_p based laplacian regularization in semi supervised learning"

Connected through hyperedge HE-3 (induced by source paper "deterministic graph cuts in subquadratic time sparse balanced and k vertex"):
  with members:
    - "a faster local algorithm for detecting bounded size cuts with applications to higher connectivity problems"
    - "computing and testing small vertex connectivity in near linear time and queries"
    - "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond"
    - "distributed edge connectivity in sublinear time"
    - "breaking quadratic time for small vertex connectivity and an approximation scheme"
    - "graph sketching against adaptive adversaries applied to the minimum degree algorithm"
    - "expander decomposition and pruning faster stronger and simpler"
    - "faster algorithms for computing maximal 2 connected subgraphs in sparse directed graphs"

Connected through hyperedge HE-4 (induced by source paper "faster energy maximization for faster maximum flow"):
  with members:
    - "solving linear programs with sqrt rank linear system solves"
    - "min cost flow in unit capacity planar graphs"
    - "flows in almost linear time via adaptive preconditioning"
    - "solving linear programs in the current matrix multiplication time"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "routing under balance"
    - "computing maximum flow with augmenting electrical flows"

Connected through hyperedge HE-5 (induced by source paper "solving directed laplacian systems in nearly linear time through sparse lu factorizations"):
  with members:
    - "sampling random spanning trees faster than matrix multiplication"
    - "faster algorithms for computing the stationary distribution simulating random walks and more"
    - "computing maximum flow with augmenting electrical flows"
    - "on fully dynamic graph sparsifiers"
    - "approximate gaussian elimination for laplacians fast sparse and simple"
    - "almost linear time algorithms for markov chains and new spectral primitives for directed graphs"
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "sparsified cholesky solvers for sdd linear systems"

Connected through hyperedge HE-6 (induced by source paper "numerical linear algebra in the sliding window model"):
  with members:
    - "nearly optimal distinct elements and heavy hitters on sliding windows"
    - "lectures on randomized numerical linear algebra"
    - "submodular optimization over sliding windows"
    - "sketches for matrix norms faster smaller and more general"
    - "submodular maximization over sliding windows"
    - "online row sampling"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "frequent directions simple and deterministic matrix sketching"

Connected through hyperedge HE-7 (induced by source paper "estimating learnability in the sublinear data regime"):
  with members:
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "spectrum estimation from samples"
    - "minimax estimation of functionals of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"
    - "new algorithms and lower bounds for monotonicity testing"
    - "the power of localization for efficiently learning linear separators with noise"
    - "optimal algorithms for testing closeness of discrete distributions"
    - "active property testing"

Connected through hyperedge HE-8 (induced by source paper "perron frobenius theory in nearly linear time positive eigenvectors m matrices graph kernels and other applications"):
  with members:
    - "stability of the lanczos method for matrix function approximation"
    - "faster algorithms for computing the stationary distribution simulating random walks and more"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "fast approximation algorithms for cut based problems in undirected graphs"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.DS(Data Structures and Algorithms)