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 "an objective function for stdp"):
  Abstract: We introduce a predictive objective function for the rate aspect of spike-timing dependent plasticity (STDP), i.e., ignoring the effects of synchrony of spikes but looking at spiking {\em rate changes}. The proposed weight update is proportional to the presynaptic spiking (or firing) rate times the {\em temporal change} of the integrated postsynaptic activity. We present an intuitive explanation for the relationship between spike-timing and weight change that arises when the weight change follows this rule. Spike-based simulations agree with the proposed relationship between spike timing and the temporal change of postsynaptic activity. They show a strong correlation between the biologically observed STDP behavior and the behavior obtained from simulations where the weight change follows the gradient of the predictive objective function.

Includes members:
  Member "towards a biologically plausible backprop":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse attentive backtracking temporal creditassignment through reminding")
  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 "early inference in energy based models approximates back propagation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep neural networks abstract like humans")
  Member "towards biologically plausible deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sideways depth parallel training of video models")

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)