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 "clustering with distributed data"):
  Abstract: We consider $K$-means clustering in networked environments (e.g., internet of things (IoT) and sensor networks) where data is inherently distributed across nodes and processing power at each node may be limited. We consider a clustering algorithm referred to as networked $K$-means, or $NK$-means, which relies only on local neighborhood information exchange. Information exchange is limited to low-dimensional statistics and not raw data at the agents. The proposed approach develops a parametric family of multi-agent clustering objectives (parameterized by $\rho$) and associated distributed $NK$-means algorithms (also parameterized by $\rho$). The $NK$-means algorithm with parameter $\rho$ converges to a set of fixed points relative to the associated multi-agent objective (designated as `generalized minima'). By appropriate choice of $\rho$, the set of generalized minima may be brought arbitrarily close to the set of Lloyd's minima. Thus, the $NK$-means algorithm may be used to compute Lloyd's minima of the collective dataset up to arbitrary accuracy.

Includes members:
  Member "communication efficient learning of deep networks from decentralized data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "distributed nonconvex multiagent optimization over time varying networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "asyspa an exact asynchronous algorithm for convex optimization over digraphs")
  Member "adding vs averaging in distributed primal dual optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "faster on device training using new federated momentum algorithm")
  Member "distributed k means algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed and proximity constrained c means for discrete coverage control")
  Member "distributed k means and k median clustering on general topologies":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "federated clustering via matrix factorization models from model averaging to gradient sharing")
  Member "distributed strongly convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "proximity without consensus in online multiagent optimization")
  Member "scalable k means":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "fast distributed gradient methods":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on local computation for optimization in multi agent systems")

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.LG(Machine Learning)