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: "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.

Connected through hyperedge HE-1 (induced by source paper "embodied synaptic plasticity with online reinforcement learning"):
  with members:
    - "synaptic plasticity dynamics for deep continuous local learning decolle"
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "deep rewiring training very sparse deep networks"
    - "proximal policy optimization algorithms"
    - "asynchronous methods for deep reinforcement learning"
    - "camkii activation supports reward based neural network optimization through hamiltonian sampling"
    - "closed loop interactions between spiking neural network and robotic simulators based on music and ros"

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.NE(Neural and Evolutionary Computing)