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: "graph sparsification via refinement sampling"
  Abstract: A graph G'(V,E') is an \eps-sparsification of G for some \eps>0, if every (weighted) cut in G' is within (1\pm \eps) of the corresponding cut in G. A celebrated result of Benczur and Karger shows that for every undirected graph G, an \eps-sparsification with O(n\log n/\e^2) edges can be constructed in O(m\log^2n) time. Applications to modern massive data sets often constrain algorithms to use computation models that restrict random access to the input. The semi-streaming model, in which the algorithm is constrained to use \tilde O(n) space, has been shown to be a good abstraction for analyzing graph algorithms in applications to large data sets. Recently, a semi-streaming algorithm for graph sparsification was presented by Anh and Guha; the total running time of their implementation is \Omega(mn), too large for applications where both space and time are important. In this paper, we introduce a new technique for graph sparsification, namely refinement sampling, that gives an \tilde{O}(m) time semi-streaming algorithm for graph sparsification. #R##N#Specifically, we show that refinement sampling can be used to design a one-pass streaming algorithm for sparsification that takes O(\log\log n) time per edge, uses O(\log^2 n) space per node, and outputs an \eps-sparsifier with O(n\log^3 n/\eps^2) edges. At a slightly increased space and time complexity, we can reduce the sparsifier size to O(n \log n/\e^2) edges matching the Benczur-Karger result, while improving upon the Benczur-Karger runtime for m=\omega(n\log^3 n). Finally, we show that an \eps-sparsifier with O(n \log n/\eps^2) edges can be constructed in two passes over the data and O(m) time whenever m =\Omega(n^{1+\delta}) for some constant \delta>0. As a by-product of our approach, we also obtain an O(m\log\log n+n \log n) time streaming algorithm to compute a sparse k-connectivity certificate of a graph.

Connected through hyperedge HE-1 (induced by source paper "sublinear algorithms for maxcut and correlation clustering"):
  with members:
    - "correlation clustering in data streams"
    - "sub exponential approximation schemes for csps from dense to almost sparse"
    - "sketching cuts in graphs and hypergraphs"
    - "the sketching complexity of graph cuts"
    - "streaming lower bounds for approximating max cut"
    - "better bounds for matchings in the streaming model"
    - "a note on no need to choose how to get both a ptas and sublinear query complexity"
    - "single pass sparsification in the streaming model with edge deletions"

Connected through hyperedge HE-2 (induced by source paper "sparse sums of positive semidefinite matrices"):
  with members:
    - "a matrix hyperbolic cosine algorithm and applications"
    - "faster spectral sparsification and numerical algorithms for sdd matrices"
    - "a parallel approximation algorithm for positive semidefinite programming"
    - "near optimal column based matrix reconstruction"
    - "graph sparsification by edge connectivity and random spanning trees"
    - "towards an sdp based approach to spectral methods a nearly linear time algorithm for graph partitioning and decomposition"
    - "a linear time algorithm for sparsification of unweighted graphs"
    - "approaching optimality for solving sdd systems"

Connected through hyperedge HE-3 (induced by source paper "the sketching complexity of graph cuts"):
  with members:
    - "sketching cuts in graphs and hypergraphs"
    - "single pass spectral sparsification in dynamic streams"
    - "distributed computation of large scale graph problems"
    - "when distributed computation is communication expensive"
    - "single pass sparsification in the streaming model with edge deletions"
    - "a nearly mlogn time solver for sdd linear systems"
    - "fast approximation algorithms for cut based problems in undirected graphs"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-4 (induced by source paper "probabilistic spectral sparsification in sublinear time"):
  with members:
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems"
    - "nearly maximum flows in nearly linear time"
    - "single pass sparsification in the streaming model with edge deletions"
    - "faster spectral sparsification and numerical algorithms for sdd matrices"
    - "a nearly mlogn time solver for sdd linear systems"
    - "a near optimal sublinear time algorithm for approximating the minimum vertex cover size"
    - "fast approximation algorithms for cut based problems in undirected graphs"

Connected through hyperedge HE-5 (induced by source paper "distributed computation of large scale graph problems"):
  with members:
    - "fast distributed pagerank computation"
    - "distributed approximation algorithms for weighted shortest paths"
    - "when distributed computation is communication expensive"
    - "can quantum communication speed up distributed computation"
    - "streaming balanced graph partitioning for random graphs"
    - "tri tri again finding triangles and small subgraphs in a distributed setting"
    - "sublinear bounds for randomized leader election"
    - "dense subgraphs on dynamic networks"

Connected through hyperedge HE-6 (induced by source paper "distributed minimum cut approximation"):
  with members:
    - "a new perspective on vertex connectivity"
    - "distributed verification and hardness of distributed approximation"
    - "minimum cuts in near linear 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)