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: "lower bounds for approximating graph parameters via communication complexity"
  Abstract: We present a new framework for proving query complexity lower bounds for graph problems via reductions from communication complexity. We illustrate our technique by giving new proofs of tight lower bounds for several graph problems: estimating the number of edges in a graph, sampling edges from an almost-uniform distribution, estimating the number of $k$-cliques in a graph, and estimating the moments of the degree distribution of a graph.

Connected through hyperedge HE-1 (induced by source paper "seeing far vs seeing wide volume complexity of local graph problems"):
  with members:
    - "an automatic speedup theorem for distributed problems"
    - "polylogarithmic time deterministic network decomposition and distributed derandomization"
    - "how much does randomness help with locally checkable problems"
    - "locality of not so weak coloring"
    - "almost global problems in the local model"
    - "the distributed complexity of locally checkable problems on paths is decidable"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "hardness of minimal symmetry breaking in distributed computing"

Connected through hyperedge HE-2 (induced by source paper "the arboricity captures the complexity of sampling edges"):
  with members:
    - "a simple sublinear time algorithm for counting arbitrary subgraphs via edge sampling"
    - "faster sublinear approximations of k cliques for low arboricity graphs"
    - "testing bounded arboricity"
    - "on approximating the number of k cliques in sublinear time"
    - "on sampling edges almost uniformly"
    - "sublinear time estimation of degree distribution moments the degeneracy connection"
    - "approximately counting triangles in sublinear time"
    - "listing all maximal cliques in large sparse real world graphs"

Connected through hyperedge HE-3 (induced by source paper "on approximating the number of k cliques in sublinear time"):
  with members:
    - "on sampling edges almost uniformly"
    - "sublinear time estimation of degree distribution moments the degeneracy connection"
    - "clique counting in mapreduce algorithms and experiments"
    - "approximately counting triangles in sublinear time"
    - "if the current clique algorithms are optimal so is valiant s parser"
    - "parallel triangle counting in massive streaming graphs"
    - "community structure and scale free collections of erdős renyi graphs"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"

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.CC(Computational Complexity)