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: "asyspa an exact asynchronous algorithm for convex optimization over digraphs"
  Abstract: This paper proposes a novel exact asynchronous subgradient-push algorithm (AsySPA) to solve an additive cost optimization problem over digraphs where each node only has access to a local convex function and updates asynchronously with an arbitrary rate. Specifically, each node of a strongly connected digraph does not wait for updates from other nodes but simply starts a new update within any bounded time interval by using local information available from its in-neighbors. "Exact" means that every node of the AsySPA can asymptotically converge to the same optimal solution, even under different update rates among nodes and bounded communication delays. To compensate uneven update rates, we design a simple mechanism to adaptively adjust stepsizes per update in each node, which is substantially different from the existing works. Then, we construct a delay-free augmented system to address asynchrony and delays, and perform the convergence analysis by proposing a generalized subgradient algorithm, which clearly has its own significance and helps us to explicitly evaluate the convergence speed of the AsySPA. Finally, we demonstrate advantages of the AsySPA over the celebrated synchronous SPA in both theory and simulation.

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"
    - "asynchronous subgradient push"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "communication efficient distributed reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "gradient tracking and variance reduction for decentralized optimization and machine learning"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "sgd general analysis and improved rates"
    - "distributed stochastic optimization with gradient tracking over strongly connected networks"
    - "on the performance of exact diffusion over adaptive networks"
    - "an exact quantized decentralized gradient descent algorithm"
    - "on the influence of bias correction on distributed stochastic optimization"
    - "stochastic gradient push for distributed deep learning"
    - "variance reduced stochastic learning by networked agents under random reshuffling"

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"
    - "asynchronous subgradient push"
    - "communication compression for decentralized training"
    - "distributed learning over unreliable networks"

Connected through hyperedge HE-4 (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"
    - "asynchronous subgradient push"
    - "distributed learning over unreliable networks"
    - "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-5 (induced by source paper "asynchronous decentralized optimization in directed networks"):
  with members:
    - "asynchronous subgradient push"
    - "pipe sgd a decentralized pipelined sgd framework for distributed deep net training"
    - "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-6 (induced by source paper "asynchronous gradient push"):
  with members:
    - "asynchronous subgradient push"
    - "asynchronous distributed optimization using a randomized alternating direction method of multipliers"
    - "gossip algorithms for distributed signal processing"

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.DC(Distributed, Parallel, and Cluster Computing)