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: "asynchronous subgradient push"
  Abstract: We consider a multi-agent framework for distributed optimization where each agent in the network has access to a local convex function and the collective goal is to achieve consensus on the parameters that minimize the sum of the agents' local functions. We propose an algorithm wherein each agent operates asynchronously and independently of the other agents in the network. When the local functions are strongly-convex with Lipschitz-continuous gradients, we show that a subsequence of the iterates at each agent converges to a neighbourhood of the global minimum, where the size of the neighbourhood depends on the degree of asynchrony in the multi-agent network. When the agents work at the same rate, convergence to the global minimizer is achieved. Numerical experiments demonstrate that Asynchronous Subgradient-Push can minimize the global objective faster than state-of-the-art synchronous first-order methods, is more robust to failing or stalling agents, and scales better with the network size.

Connected through hyperedge HE-1 (induced by source paper "asynchronous policy evaluation in distributed reinforcement learning over networks"):
  with members:
    - "multi agent reinforcement learning a selective overview of theories and algorithms"
    - "asynchronous decentralized optimization in directed networks"
    - "distributed metropolis sampler with optimal parallelism"
    - "value propagation for decentralized networked deep multi agent reinforcement learning"
    - "distributed off policy actor critic reinforcement learning with policy consensus"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "communication efficient distributed reinforcement learning"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-2 (induced by source paper "central server free federated learning over single sided trust social networks"):
  with members:
    - "decentralized online learning take benefits from others data without sharing your own to track global trend"
    - "introduction to online convex optimization"
    - "federated machine learning concept and applications"
    - "pipe sgd a decentralized pipelined sgd framework for distributed deep net training"
    - "federated learning strategies for improving communication efficiency"
    - "don t use large mini batches use local sgd"
    - "stochastic gradient push for distributed deep learning"
    - "cola decentralized linear learning"

Connected through hyperedge HE-3 (induced by source paper "decentralized stochastic gradient tracking for non convex empirical risk minimization"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "distributed stochastic optimization with gradient tracking over strongly connected networks"
    - "asynchronous decentralized optimization in directed networks"
    - "on the performance of exact diffusion over adaptive networks"
    - "decentralized deep learning with arbitrary communication compression"
    - "communication compression for decentralized training"
    - "distributed learning over unreliable networks"
    - "asyspa an exact asynchronous algorithm for convex optimization over digraphs"

Connected through hyperedge HE-4 (induced by source paper "gossip based actor learner architectures for deep reinforcement learning"):
  with members:
    - "elf opengo an analysis and open reimplementation of alphazero"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "accelerated methods for deep reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "distributed prioritized experience replay"
    - "stochastic gradient push for distributed deep learning"
    - "efficient parallel methods for deep reinforcement learning"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"

Connected through hyperedge HE-5 (induced by source paper "decentralized stochastic gradient tracking for empirical risk minimization"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "distributed stochastic optimization with gradient tracking over strongly connected networks"
    - "asynchronous decentralized optimization in directed networks"
    - "on the influence of bias correction on distributed stochastic optimization"
    - "distributed learning over unreliable networks"
    - "asyspa an exact asynchronous algorithm for convex optimization over digraphs"
    - "stochastic gradient push for distributed deep learning"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-6 (induced by source paper "asynchronous decentralized optimization in directed networks"):
  with members:
    - "pipe sgd a decentralized pipelined sgd framework for distributed deep net training"
    - "asyspa an exact asynchronous algorithm for convex optimization over digraphs"
    - "cola decentralized linear learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "asynchronous stochastic gradient descent with delay compensation"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-7 (induced by source paper "stochastic gradient push for distributed deep learning"):
  with members:
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "scaling neural machine translation"
    - "exploring the limits of weakly supervised pretraining"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "attention is all you need"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "convolutional sequence to sequence learning"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"

Connected through hyperedge HE-8 (induced by source paper "asyspa an exact asynchronous algorithm for convex optimization over digraphs"):
  with members:
    - "distributed discrete time optimization in multiagent networks using only sign of relative state"
    - "asynchronous decentralized optimization in directed networks"
    - "distributed convex optimization with inequality constraints over time varying unbalanced digraphs"
    - "distributed nonconvex multiagent optimization over time varying networks"
    - "asynchronous distributed optimization via randomized dual proximal gradient"
    - "asynchronous adaptation and learning over networks part i modeling and stability analysis"
    - "average consensus on general strongly connected digraphs"

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.MA(Multiagent Systems)