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 decentralized optimization in directed networks"
  Abstract: A popular asynchronous protocol for decentralized optimization is randomized gossip where a pair of neighbors concurrently update via pairwise averaging. In practice, this creates deadlocks and is vulnerable to information delays. It can also be problematic if a node is unable to response or has only access to its private-preserved local dataset. To address these issues simultaneously, this paper proposes an asynchronous decentralized algorithm, i.e. APPG, with {\em directed} communication where each node updates {\em asynchronously} and independently of any other node. If local functions are strongly-convex with Lipschitz-continuous gradients, each node of APPG converges to the same optimal solution at a rate of $O(\lambda^k)$, where $\lambda\in(0,1)$ and the virtual counter $k$ increases by 1 no matter on which node updates. The superior performance of APPG is validated on a logistic regression problem against state-of-the-art methods in terms of linear speedup and system implementations.

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"
    - "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"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-2 (induced by source paper "moniqua modulo quantized communication in decentralized sgd"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "distributed learning with sublinear communication"
    - "texttt deepsqueeze decentralization meets error compensated compression"
    - "communication compression for decentralized training"
    - "high accuracy low precision training"
    - "quantized decentralized consensus optimization"
    - "distributed learning over unreliable networks"
    - "sparsified sgd with memory"

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"
    - "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"
    - "asyspa an exact asynchronous algorithm for convex optimization over digraphs"

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"
    - "on the influence of bias correction on distributed stochastic optimization"
    - "asynchronous subgradient push"
    - "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-5 (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 subgradient push"
    - "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.DC(Distributed, Parallel, and Cluster Computing)