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 "distributed nesterov gradient methods over arbitrary graphs"):
  Abstract: In this letter, we introduce a distributed Nesterov gradient method, ABN, that does not require doubly stochastic weights. Instead, the implementation is based on a simultaneous application of both row- and column-stochastic weights that makes ABN applicable to arbitrary (strongly-connected) graphs. Since constructing column-stochastic weights needs additional information (the number of outgoing neighbors), not available in certain communication protocols, we derive a variation, FROZEN, that only requires row-stochastic weights, but at the expense of additional iterations for eigenvector estimation. We numerically study these algorithms for various objective functions and network parameters and show that the proposed distributed Nesterov gradient methods achieve acceleration compared to the current state-of-the-art methods for distributed optimization.

Includes members:
  Member "multi agent reinforcement learning via double averaging primal dual optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regret bounds for decentralized learning in cooperative multi agent dynamical systems")
  Member "convergence rates of distributed nesterov like gradient methods on random networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a unification and generalization of exact distributed first order methods")
  Member "average consensus on general strongly connected digraphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "asyspa an exact asynchronous algorithm for convex optimization over digraphs")
  Member "fast distributed gradient methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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)