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: "fast generation of random spanning trees and the effective resistance metric"
  Abstract: We present a new algorithm for generating a uniformly random spanning tree in an undirected graph. Our algorithm samples such a tree in expected O(m4/3+o(1)) time. This improves over the best previously known bound of min(O(m[EQUATION]n), O(nω)) - that follows from the work of Kelner and Madry [FOCS'09] and of Colbourn et al. [J. Algorithms'96] - whenever the input graph is sufficiently sparse.#R##N##R##N#At a high level, our result stems from carefully exploiting the interplay of random spanning trees, random walks, and the notion of effective resistance, as well as from devising a way to algorithmically relate these concepts to the combinatorial structure of the graph. This involves, in particular, establishing a new connection between the effective resistance metric and the cut structure of the underlying graph.

Connected through hyperedge HE-1 (induced by source paper "a spectral approach to network design"):
  with members:
    - "network design for s t effective resistance"
    - "new notions and constructions of sparsification for graphs and hypergraphs"
    - "on a generalization of iterated and randomized rounding"
    - "proportional volume sampling and approximation algorithms for a optimal design"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "the gram schmidt walk a cure for the banaszczyk blues"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "graph clustering using effective resistance"

Connected through hyperedge HE-2 (induced by source paper "fully dynamic spectral vertex sparsifiers and applications"):
  with members:
    - "dynamic low stretch trees via dynamic low diameter decompositions"
    - "graph reduction by local variation"
    - "kirchhoff index as a measure of edge centrality in weighted networks nearly linear time algorithms"
    - "dynamic effective resistances and approximate schur complement on separable graphs"
    - "on solving linear systems in sublinear time"
    - "spectrally approximating large graphs with smaller graphs"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "density independent algorithms for sparsifying k step random walks"

Connected through hyperedge HE-3 (induced by source paper "dynamic streaming spectral sparsification in nearly linear time and space"):
  with members:
    - "faster spectral sparsification in dynamic streams"
    - "optimal lower bounds for distributed and streaming spanning forest computation"
    - "spectral subspace sparsification"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "density independent algorithms for sparsifying k step random walks"

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 "faster spectral sparsification in dynamic streams"):
  with members:
    - "dynamic streaming spectral sparsification in nearly linear time and space"
    - "spectral subspace sparsification"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "density independent algorithms for sparsifying k step random walks"
    - "graph clustering using effective resistance"
    - "edit distance sketching streaming and document exchange"

Connected through hyperedge HE-6 (induced by source paper "robust and sample optimal algorithms for psd low rank approximation"):
  with members:
    - "a quantum inspired classical algorithm for recommendation systems"
    - "sublinear time numerical linear algebra for structured matrices"
    - "sample optimal low rank approximation of distance matrices"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "quantum inspired low rank stochastic regression with logarithmic dependence on the dimension"
    - "quantum inspired sublinear classical algorithms for solving low rank linear systems"
    - "sublinear time low rank approximation of distance matrices"
    - "sublinear time low rank approximation of positive semidefinite matrices"

Connected through hyperedge HE-7 (induced by source paper "a distributed laplacian solver and its applications to electrical flow and random spanning tree computation"):
  with members:
    - "current flow group closeness centrality for complex networks"
    - "pooling or sampling collective dynamics for electrical flow estimation"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "decentralized random walk based data collection in networks"
    - "sampling random spanning trees faster than matrix multiplication"
    - "estimating current flow closeness centrality with a multigrid laplacian solver"
    - "computing maximum flow with augmenting electrical flows"
    - "a fast distributed solver for symmetric diagonally dominant linear equations"

Connected through hyperedge HE-8 (induced by source paper "faster p norm minimizing flows via smoothed q norm problems"):
  with members:
    - "fast provably convergent irls algorithm for p norm linear regression"
    - "flows in almost linear time via adaptive preconditioning"
    - "coordinate methods for accelerating ell_ infty regression and faster approximate maximum flow"
    - "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"
    - "approximate undirected maximum flows in o m polylog n time"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"

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)