human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "hamiltonian synaptic sampling in a model for reward gated network plasticity"):
  Abstract: Experimental data show that synaptic connections are subject to stochastic processes, and that neural codes drift on larger time scales. These data suggest to consider besides maximum likelihood learning also sampling models for network plasticity (synaptic sampling), where the current network connectivity and parameter values are viewed as a sample from a Markov chain, whose stationary distribution captures the invariant properties of network plasticity. However convergence to this stationary distribution may be rather slow if synaptic sampling carries out Langevin sampling. We show here that data on the molecular basis of synaptic plasticity, specifically on the role of CaMKII in its activated form, support a substantially more efficient Hamiltonian sampling of network configurations. We apply this new conceptual and mathematical framework to the analysis of reward-gated network plasticity, and show in a concrete example based on experimental data that Hamiltonian sampling speeds up the convergence to well-functioning network configurations. We also show that a regulation of the temperature of the sampling process provides a link between reinforcement learning and global network optimization through simulated annealing.

Includes members:
  Member "equilibrated adaptive learning rates for non convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deam adaptive momentum with discriminative weight for stochastic optimization")
  Member "network plasticity as bayesian inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spiking neural networks for inference and learning a memristor based design perspective")
  Member "identifying and attacking the saddle point problem in high dimensional non convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")

Question: Please tell me which class the source paper 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.NE(Neural and Evolutionary Computing)