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 "randomized distributed mean estimation accuracy vs communication"):
  Abstract: We consider the problem of estimating the arithmetic average of a finite collection of real vectors stored in a distributed fashion across several compute nodes subject to a communication budget constraint. Our analysis does not rely on any statistical assumptions about the source of the vectors. This problem arises as a subproblem in many applications, including reduce-all operations within algorithms for distributed and federated optimization and learning. We propose a flexible family of randomized algorithms exploring the trade-off between expected communication cost and estimation error. Our family contains the full-communication and zero-error method on one extreme, and an $\epsilon$-bit communication and ${\cal O}\left(1/(\epsilon n)\right)$ error method on the opposite extreme. In the special case where we communicate, in expectation, a single bit per coordinate of each vector, we improve upon existing results by obtaining $\mathcal{O}(r/n)$ error, where $r$ is the number of bits used to represent a floating point value.

Includes members:
  Member "federated learning strategies for improving communication efficiency":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "trends and advancements in deep neural network communication")
  Member "orthogonal random features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "coke communication censored kernel learning for decentralized non parametric learning")
  Member "communication efficient learning of deep networks from decentralized data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "distributed mean estimation with limited communication":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rpn a residual pooling network for efficient federated learning")
  Member "federated optimization distributed machine learning for on device intelligence":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "distributed optimization with arbitrary local solvers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adaptivity of stochastic gradient methods for nonconvex optimization")
  Member "communication lower bounds for statistical estimation problems via a distributed data processing inequality":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "locally private hypothesis selection")
  Member "on communication cost of distributed statistical estimation and dimensionality":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the communication complexity of optimization")

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.DC(Distributed, Parallel, and Cluster Computing)