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: "subgraph sparsification and nearly optimal ultrasparsifiers"
  Abstract: We consider a variation of the spectral sparsification problem where we are required to keep a subgraph of the original graph. Formally, given a union of two weighted graphs $G$ and $W$ and an integer $k$, we are asked to find a $k$-edge weighted graph $W_k$ such that $G+W_k$ is a good spectral sparsifer of $G+W$. We will refer to this problem as the subgraph (spectral) sparsification. We present a nontrivial condition on $G$ and $W$ such that a good sparsifier exists and give a polynomial time algorithm to find the sparsifer. #R##N#%$O(\frac{n}{k})\log n \tilde{O}(\log \log n)$ As a significant application of our technique, we show that for each positive integer $k$, every $n$-vertex weighted graph has an $(n-1+k)$-edge spectral sparsifier with relative condition number at most $\frac{n}{k} \log n \tilde{O}(\log\log n)$ where $\tilde{O}()$ hides lower order terms. Our bound is within a factor of $\tilde{O}(\log \log n)$ from optimal. This nearly settles a question left open by Spielman and Teng about ultrasparsifiers, which is a key component in their nearly linear-time algorithms for solving diagonally dominant symmetric linear systems. #R##N#We also present another application of our technique to spectral optimization in which the goal is to maximize the algebraic connectivity of a graph (e.g. turn it into an expander) with a limited number of edges.

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 "grass spectral sparsification leveraging scalable spectral perturbation analysis"):
  with members:
    - "similarity aware spectral sparsification by edge filtering"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "almost linear time algorithms for markov chains and new spectral primitives for directed graphs"
    - "partitioning well clustered graphs spectral clustering works"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"
    - "a note on preconditioning by low stretch spanning trees"

Connected through hyperedge HE-3 (induced by source paper "network design for s t effective resistance"):
  with members:
    - "proportional volume sampling and approximation algorithms for a optimal design"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "fast generation of random spanning trees and the effective resistance metric"
    - "effective resistance reducing flows spectrally thin trees and asymmetric tsp"
    - "parameters of two prover one round game and the hardness of connectivity problems"
    - "iterative rounding approximation algorithms for degree bounded node connectivity network design"
    - "approximating minimum cost k node connected subgraphs via independence free graphs"
    - "reductions between expansion problems"

Connected through hyperedge HE-4 (induced by source paper "maximizing the number of spanning trees in a connected graph"):
  with members:
    - "current flow group closeness centrality for complex networks"
    - "fully dynamic spectral vertex sparsifiers and applications"
    - "kirchhoff index as a measure of edge centrality in weighted networks nearly linear time algorithms"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "a nearly linear time algorithm for submodular maximization with a knapsack constraint"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "subdeterminant maximization via nonconvex relaxations and anti concentration"
    - "determinant preserving sparsification of sddm matrices with applications to counting and sampling spanning trees"

Connected through hyperedge HE-5 (induced by source paper "spectral subspace sparsification"):
  with members:
    - "dynamic effective resistances and approximate schur complement on separable graphs"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "improved guarantees for vertex sparsification in planar graphs"
    - "determinant preserving sparsification of sddm matrices with applications to counting and sampling spanning trees"
    - "sampling random spanning trees faster than matrix multiplication"
    - "graph minors for preserving terminal distances approximately lower and upper bounds"
    - "approximate gaussian elimination for laplacians fast sparse and simple"
    - "vertex sparsifiers new results from old techniques"

Connected through hyperedge HE-6 (induced by source paper "optimal approximate matrix product in terms of stable rank"):
  with members:
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "improved distributed principal component analysis"
    - "uniform sampling for matrix approximation"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "lower bounds for oblivious subspace embeddings"
    - "low rank approximation and regression in input sparsity time"
    - "new probabilistic bounds on eigenvalues and eigenvectors of random kernel matrices"

Connected through hyperedge HE-7 (induced by source paper "simple parallel and distributed algorithms for spectral graph sparsification"):
  with members:
    - "an efficient parallel solver for sdd linear systems"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "faster spectral sparsification and numerical algorithms for sdd matrices"
    - "a nearly mlogn time solver for sdd linear systems"
    - "towards an sdp based approach to spectral methods a nearly linear time algorithm for graph partitioning and decomposition"
    - "approaching optimality for solving sdd systems"
    - "twice ramanujan sparsifiers"
    - "graph sparsification by effective resistances"

Connected through hyperedge HE-8 (induced by source paper "single pass sparsification in the streaming model with edge deletions"):
  with members:
    - "graph sparsification in the semi streaming model"
    - "twice ramanujan sparsifiers"
    - "spectral sparsification of graphs"
    - "graph sparsification by effective resistances"

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.DM(Discrete Mathematics)