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: "forward table based presynaptic event triggered spike timing dependent plasticity"
  Abstract: Spike-timing-dependent plasticity (STDP) incurs both causal and acausal synaptic weight updates, for negative and positive time differences between pre-synaptic and post-synaptic spike events. For realizing such updates in neuromorphic hardware, current implementations either require forward and reverse lookup access to the synaptic connectivity table, or rely on memory-intensive architectures such as crossbar arrays. We present a novel method for realizing both causal and acausal weight updates using only forward lookup access of the synaptic connectivity table, permitting memory-efficient implementation. A simplified implementation in FPGA, using a single timer variable for each neuron, closely approximates exact STDP cumulative weight updates for neuron refractory periods greater than 10 ms, and reduces to exact STDP for refractory periods greater than the STDP time window. Compared to conventional crossbar implementation, the forward table-based implementation leads to substantial memory savings for sparsely connected networks supporting scalable neuromorphic systems with fully reconfigurable synaptic connectivity and plasticity.

Connected through hyperedge HE-1 (induced by source paper "neural and synaptic array transceiver a brain inspired computing framework for embedded learning"):
  with members:
    - "contrastive hebbian learning with random feedback weights"
    - "membrane dependent neuromorphic learning rule for unsupervised spike pattern detection"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "learning in the machine random backpropagation and the deep learning channel"
    - "stochastic synapses enable efficient brain inspired learning machines"
    - "supervised learning based on temporal coding in spiking neural networks"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-2 (induced by source paper "event driven random back propagation enabling neuromorphic deep learning machines"):
  with members:
    - "learning in the machine random backpropagation and the deep learning channel"
    - "stochastic synapses enable efficient brain inspired learning machines"
    - "supervised learning based on temporal coding in spiking neural networks"
    - "training deep spiking neural networks using backpropagation"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "gibbs sampling with low power spiking digital neurons"
    - "how important is weight symmetry in backpropagation"
    - "difference target propagation"

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)