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: "distributed nonconvex multiagent optimization over time varying networks"
  Abstract: We study nonconvex distributed optimization in multiagent networks where the communications between nodes is modeled as a time-varying sequence of arbitrary digraphs. We introduce a novel broadcast-based distributed algorithmic framework for the (constrained) minimization of the sum of a smooth (possibly nonconvex and nonseparable) function, i.e., the agents' sum-utility, plus a convex (possibly nonsmooth and nonseparable) regularizer. The latter is usually employed to enforce some structure in the solution, typically sparsity. The proposed method hinges on Successive Convex Approximation (SCA) techniques coupled with i) a tracking mechanism instrumental to locally estimate the gradients of agents' cost functions; and ii) a novel broadcast protocol to disseminate information and distribute the computation among the agents. Asymptotic convergence to stationary solutions is established. A key feature of the proposed algorithm is that it neither requires the double-stochasticity of the consensus matrices (but only column stochasticity) nor the knowledge of the graph sequence to implement. To the best of our knowledge, the proposed framework is the first broadcast-based distributed algorithm for convex and nonconvex constrained optimization over arbitrary, time-varying digraphs. Numerical results show that our algorithm outperforms current schemes on both convex and nonconvex problems.

Connected through hyperedge HE-1 (induced by source paper "clustering with distributed data"):
  with members:
    - "communication efficient learning of deep networks from decentralized data"
    - "adding vs averaging in distributed primal dual optimization"
    - "distributed k means algorithm"
    - "distributed k means and k median clustering on general topologies"
    - "distributed strongly convex optimization"
    - "scalable k means"
    - "fast distributed gradient methods"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-2 (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"
    - "asynchronous subgradient push"
    - "distributed convex optimization with inequality constraints over time varying unbalanced digraphs"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "distributed big data optimization via block iterative convexification and averaging"):
  with members:
    - "coordinate descent diffusion learning by networked agents"
    - "a randomized primal distributed algorithm for partitioned and big data non convex optimization"
    - "doubly random parallel stochastic methods for large scale learning"
    - "next in network nonconvex optimization"

Connected through hyperedge HE-4 (induced by source paper "distributed mechanism design with learning guarantees"):
  with members:
    - "mechanism design for resource allocation in networks with intergroup competition and intragroup sharing"

Connected through hyperedge HE-5 (induced by source paper "distributed convex optimization with inequality constraints over time varying unbalanced digraphs"):
  with members:
    - "networked parallel algorithms for robust convex optimization via the scenario approach"
    - "distributed random fixed projected algorithm for constrained optimization over digraphs"
    - "explicit convergence rate of a distributed alternating direction method of multipliers"
    - "average consensus on general strongly connected digraphs"

Connected through hyperedge HE-6 (induced by source paper "distributed optimization of convex sum of non convex functions"):
  with members:
    - "distributed optimization for client server architecture with negative gradient weights"
    - "next in network nonconvex optimization"

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)