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 "boltzmann machines for time series"):
  Abstract: We review Boltzmann machines extended for time-series. These models often have recurrent structure, and back propagration through time (BPTT) is used to learn their parameters. The per-step computational complexity of BPTT in online learning, however, grows linearly with respect to the length of preceding time-series (i.e., learning rule is not local in time), which limits the applicability of BPTT in online learning. We then review dynamic Boltzmann machines (DyBMs), whose learning rule is local in time. DyBM's learning rule relates to spike-timing dependent plasticity (STDP), which has been postulated and experimentally confirmed for biological neural networks.

Includes members:
  Member "boltzmann machines and energy based models":
    also connected through other hyperedges:
      (none)
  Member "regularized dynamic boltzmann machine with delay pruning for unsupervised learning of temporal sequences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning binary or real valued time series via spike timing dependent plasticity")
  Member "learning binary or real valued time series via spike timing dependent plasticity":
    also connected through other hyperedges:
      (none)
  Member "equilibrium propagation bridging the gap between energy based models and backpropagation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning in memristive nanowire networks")
  Member "stdp as presynaptic activity times rate of change of postsynaptic activity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using inspiration from synaptic plasticity rules to optimize traffic flow in distributed engineered networks")
  Member "an objective function for stdp":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spatio temporal backpropagation for training high performance spiking neural networks")
  Member "learning dynamic boltzmann machines with spike timing dependent plasticity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "time discounting convolution for event sequences with ambiguous timestamps")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)