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: "next in network nonconvex optimization"
  Abstract: We study nonconvex distributed optimization in multi-agent networks with time-varying (nonsymmetric) connectivity. We introduce the first algorithmic framework for the distributed minimization of the sum of a smooth (possibly nonconvex and nonseparable) function - 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 techniques while leveraging dynamic consensus as a mechanism to distribute the computation among the agents: each agent first solves (possibly inexactly) a local convex approximation of the nonconvex original problem, and then performs local averaging operations. Asymptotic convergence to (stationary) solutions of the nonconvex problem is established. Our algorithmic framework is then customized to a variety of convex and nonconvex problems in several fields, including signal processing, communications, networking, and machine learning. Numerical results show that the new method compares favorably to existing distributed algorithms on both convex and nonconvex problems.

Connected through hyperedge HE-1 (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"
    - "asyspa an exact asynchronous algorithm for convex optimization over digraphs"
    - "stochastic gradient push for distributed deep learning"

Connected through hyperedge HE-2 (induced by source paper "supervised learning under distributed features"):
  with members:
    - "coordinate descent diffusion learning by networked agents"
    - "variance reduced stochastic learning by networked agents under random reshuffling"
    - "variance reduced stochastic learning under random reshuffling"
    - "distributed learning for stochastic generalized nash equilibrium problems"
    - "sdca without duality regularization and individual convexity"
    - "doubly random parallel stochastic methods for large scale learning"
    - "dictionary learning over distributed models"
    - "multi agent distributed optimization via inexact consensus admm"

Connected through hyperedge HE-3 (induced by source paper "collective learning"):
  with members:
    - "asynchronous distributed learning from constraints"
    - "federated learning strategies for improving communication efficiency"
    - "revisiting distributed synchronous sgd"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "communication efficient learning of deep networks from decentralized data"
    - "tensorflow a system for large scale machine learning"
    - "federated optimization distributed optimization beyond the datacenter"
    - "mllib machine learning in apache spark"

Connected through hyperedge HE-4 (induced by source paper "an introduction to decentralized stochastic optimization with gradient tracking"):
  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"
    - "an exact quantized decentralized gradient descent algorithm"
    - "on the influence of bias correction on distributed stochastic optimization"
    - "tutorial on dynamic average consensus the problem its applications and the algorithms"
    - "stochastic gradient push for distributed deep learning"
    - "variance reduced stochastic learning by networked agents under random reshuffling"

Connected through hyperedge HE-5 (induced by source paper "linear speedup in saddle point escape for decentralized non convex optimization"):
  with members:
    - "distributed learning in non convex environments part ii polynomial escape from saddle points"
    - "on nonconvex optimization for machine learning gradients stochasticity and saddle points"
    - "escaping saddles with stochastic gradients"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "communication efficient distributed dual coordinate ascent"
    - "on the learning behavior of adaptive networks part ii performance analysis"

Connected through hyperedge HE-6 (induced by source paper "distributed learning in non convex environments part ii polynomial escape from saddle points"):
  with members:
    - "on nonconvex optimization for machine learning gradients stochasticity and saddle points"
    - "distributed stochastic optimization with gradient tracking over strongly connected networks"
    - "escaping saddles with stochastic gradients"
    - "how to escape saddle points efficiently"
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"

Connected through hyperedge HE-7 (induced by source paper "asynchronous distributed learning from constraints"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "gradient diversity a key ingredient for scalable distributed learning"
    - "federated multi task learning"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-8 (induced by source paper "learn electronic health records by fully decentralized federated learning"):
  with members:
    - "a survey on federated learning systems vision hype and reality for data privacy and protection"
    - "federated machine learning concept and applications"
    - "communication efficient learning of deep networks from decentralized data"

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)