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: "sketching cuts in graphs and hypergraphs"
  Abstract: Sketching and streaming algorithms are in the forefront of current research directions for cut problems in graphs. In the streaming model, we show that $(1-\epsilon)$-approximation for Max-Cut must use $n^{1-O(\epsilon)}$ space; moreover, beating $4/5$-approximation requires polynomial space. For the sketching model, we show that $r$-uniform hypergraphs admit a $(1+\epsilon)$-cut-sparsifier (i.e., a weighted subhypergraph that approximately preserves all the cuts) with $O(\epsilon^{-2} n (r+\log n))$ edges. We also make first steps towards sketching general CSPs (Constraint Satisfaction Problems).

Connected through hyperedge HE-1 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism"):
  with members:
    - "storing and querying large scale spatio temporal graphs with high throughput edge insertions"
    - "parallel batch dynamic graph connectivity"
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "slim fly a cost effective low diameter network topology"
    - "red blue pebbling revisited near optimal parallel matrix matrix multiplication"
    - "dynamic algorithms for the massively parallel computation model"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"

Connected through hyperedge HE-2 (induced by source paper "hardness of distributed optimization"):
  with members:
    - "small cuts and connectivity certificates a fault tolerant approach"
    - "fast and efficient distributed computation of hamiltonian cycles in random graphs"
    - "a distributed algorithm for finding hamiltonian cycles in random graphs in o log n time"
    - "new distributed algorithms in almost mixing time via transformations from parallel algorithms"
    - "parameterized distributed algorithms"
    - "distributed exact weighted all pairs shortest paths in near linear time"
    - "detecting cliques in congest networks"
    - "simple and local independent set approximation"

Connected through hyperedge HE-3 (induced by source paper "new notions and constructions of sparsification for graphs and hypergraphs"):
  with members:
    - "spectral sparsification of hypergraphs"
    - "finding a bounded degree expander inside a dense one"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "an efficient algorithm for unweighted spectral graph sparsification"
    - "new constructive aspects of the lovasz local lemma"
    - "twice ramanujan sparsifiers"
    - "graph sparsification by effective resistances"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-4 (induced by source paper "sparsification of binary csps"):
  with members:
    - "spectral sparsification of hypergraphs"
    - "best case and worst case sparsifiability of boolean csps"
    - "on sketching quadratic forms"
    - "graph sparsification in the semi streaming model"
    - "twice ramanujan sparsifiers"
    - "graph sparsification by effective resistances"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-5 (induced by source paper "metric sublinear algorithms via linear sampling"):
  with members:
    - "sublinear algorithms for maxcut and correlation clustering"
    - "tight space approximation tradeoff for the multi pass streaming set cover problem"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"
    - "distributed coverage maximization via sketching"
    - "almost optimal streaming algorithms for coverage problems"
    - "towards tight bounds for the streaming set cover problem"
    - "densest subgraph in dynamic graph streams"
    - "tight bounds for linear sketches of approximate matchings"

Connected through hyperedge HE-6 (induced by source paper "an optimal space lower bound for approximating max cut"):
  with members:
    - "the sketching complexity of graph and hypergraph counting"
    - "estimating graph parameters from random order streams"
    - "on estimating maximum matching size in graph streams"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "maximum matching in turnstile streams"
    - "streaming lower bounds for approximating max cut"
    - "single pass spectral sparsification in dynamic streams"
    - "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"

Connected through hyperedge HE-7 (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"
    - "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"
    - "graph sparsification via refinement sampling"

Connected through hyperedge HE-8 (induced by source paper "spectral sparsification of hypergraphs"):
  with members:
    - "cheeger inequalities for submodular transformations"
    - "hypergraph markov operators eigenvalues and approximation algorithms"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "learning pseudo boolean k dnf and submodular functions"
    - "submodular functions are noise stable"
    - "fast approximation algorithms for cut based problems in undirected graphs"
    - "privately releasing conjunctions and the statistical query barrier"
    - "twice ramanujan sparsifiers"

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)