human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "compressive sensing for cut improvement and local clustering"):
  Abstract: We show how one can phrase the cut improvement problem for graphs as a sparse recovery problem, whence one can use algorithms originally developed for use in compressive sensing (such as SubspacePursuit or CoSaMP) to solve it. We show that this approach to cut improvement is fast, both in theory and practice and moreover enjoys statistical guarantees of success when applied to graphs drawn from probabilistic models such as the Stochastic Block Model. Using this new cut improvement approach, which we call ClusterPursuit, as an algorithmic primitive we then propose new methods for local clustering and semi-supervised clustering, which enjoy similar guarantees of success and speed. Finally, we verify the promise of our approach with extensive numerical benchmarking.

Includes members:
  Member "statistical guarantees for local graph clustering":
    also connected through other hyperedges:
      (none)
  Member "power weighted shortest paths for clustering euclidean data":
    also connected through other hyperedges:
      (none)
  Member "flow based local graph clustering with better seed set inclusion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "localized flow based clustering in hypergraphs")
  Member "capacity releasing diffusion for speed and locality":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "local hypergraph clustering using capacity releasing diffusion")
  Member "an optimization approach to locally biased graph algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning resolution parameters for graph clustering")
  Member "a simple and strongly local flow based method for cut improvement":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "localized flow based clustering in hypergraphs")
  Member "detecting overlapping communities from local spectral subspaces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimizing generalized pagerank methods for seed expansion community detection")
  Member "computing heat kernel pagerank and a local clustering algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient estimation of heat kernel pagerank for local clustering")

Question: Please tell me which class the source paper 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.IT(Information Theory)