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: "simple parallel and distributed algorithms for spectral graph sparsification"
  Abstract: We describe a simple algorithm for spectral graph sparsification, based on iterative computations of weighted spanners and uniform sampling. Leveraging the algorithms of Baswana and Sen for computing spanners, we obtain the first distributed spectral sparsification algorithm. We also obtain a parallel algorithm with improved work and time guarantees. Combining this algorithm with the parallel framework of Peng and Spielman for solving symmetric diagonally dominant linear systems, we get a parallel solver which is much closer to being practical and significantly more efficient in terms of the total work.

Connected through hyperedge HE-1 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics"):
  with members:
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "active access a mechanism for high performance distributed data centric computations"
    - "communication efficient jaccard similarity for high performance distributed genome comparisons"
    - "local computation algorithms for spanners"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"

Connected through hyperedge HE-2 (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-3 (induced by source paper "low diameter graph decompositions by approximate distance computation"):
  with members:
    - "dynamic low stretch trees via dynamic low diameter decompositions"
    - "a faster distributed single source shortest paths algorithm"
    - "a hierarchy of lower bounds for sublinear additive spanners"
    - "hopsets with constant hopbound and applications to approximate shortest paths"
    - "on the complexity of local distributed graph problems"
    - "parallel metric tree embedding based on an algebraic view on moore bellman ford"
    - "near optimal distributed maximum flow"
    - "parallel graph decompositions using random shifts"

Connected through hyperedge HE-4 (induced by source paper "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"):
  with members:
    - "current flow group closeness centrality for complex networks"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "kirchhoff index as a measure of edge centrality in weighted networks nearly linear time algorithms"
    - "fully dynamic effective resistances"
    - "spectrum approximation beyond fast matrix multiplication algorithms and hardness"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "nearly tight bounds for sandpile transience on the grid"

Connected through hyperedge HE-5 (induced by source paper "effective resistance preserving directed graph symmetrization"):
  with members:
    - "a multiscale pyramid transform for graph signals"
    - "clustering and community detection in directed networks a survey"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "graph sparsification by effective resistances"

Connected through hyperedge HE-6 (induced by source paper "finding a bounded degree expander inside a dense one"):
  with members:
    - "expanders via local edge flips"
    - "simple fast and deterministic gossip and rumor spreading"
    - "global computation in a poorly connected world fast rumor spreading with no dependence on conductance"
    - "twice ramanujan sparsifiers"

Connected through hyperedge HE-7 (induced by source paper "efficient widetilde o n epsilon spectral sketches for the laplacian and its pseudoinverse"):
  with members:
    - "optimality of the johnson lindenstrauss lemma"
    - "approximate gaussian elimination for laplacians fast sparse and simple"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "on sketching quadratic forms"
    - "powers of tensors and fast matrix multiplication"
    - "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"

Connected through hyperedge HE-8 (induced by source paper "on fully dynamic graph sparsifiers"):
  with members:
    - "sparse sums of positive semidefinite matrices"
    - "unifying and strengthening hardness for dynamic problems via the online matrix vector multiplication conjecture"
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "constructing linear sized spectral sparsification in almost linear time"
    - "algorithms for lipschitz learning on graphs"
    - "sketching cuts in graphs and hypergraphs"
    - "approximate undirected maximum flows in o m polylog n time"

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)