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: "on the performance of exact diffusion over adaptive networks"
  Abstract: Various bias-correction methods such as EXTRA, DIGing, and exact diffusion have been proposed recently to solve distributed deterministic optimization problems. These methods employ constant step-sizes and converge linearly to the {\em exact} solution under proper conditions. However, their performance under stochastic and adaptive settings remains unclear. It is still unknown whether the bias-correction is necessary over adaptive networks. By studying exact diffusion and examining its steady-state performance under stochastic scenarios, this paper provides affirmative results. It is shown that the correction step in exact diffusion leads to better steady-state performance than traditional methods. It is also analytically shown the superiority of exact diffusion is more evident over badly-connected network topologies.

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"
    - "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"
    - "variance reduced stochastic learning by networked agents under random reshuffling"

Connected through hyperedge HE-2 (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"
    - "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-3 (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"
    - "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"
    - "variance reduced stochastic learning under random reshuffling"

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.LG(Machine Learning)