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 "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"):
  Abstract: The way how recurrently connected networks of spiking neurons in the brain acquire powerful information processing capabilities through learning has remained a mystery. This lack of understanding is linked to a lack of learning algorithms for recurrent networks of spiking neurons (RSNNs) that are both functionally powerful and can be implemented by known biological mechanisms. Since RSNNs are simultaneously a primary target for implementations of brain-inspired circuits in neuromorphic hardware, this lack of algorithmic insight also hinders technological progress in that area. The gold standard for learning in recurrent neural networks in machine learning is back-propagation through time (BPTT), which implements stochastic gradient descent with regard to a given loss function. But BPTT is unrealistic from a biological perspective, since it requires a transmission of error signals backwards in time and in space, i.e., from post- to presynaptic neurons. We show that an online merging of locally available information during a computation with suitable top-down learning signals in real-time provides highly capable approximations to BPTT. For tasks where information on errors arises only late during a network computation, we enrich locally available information through feedforward eligibility traces of synapses that can easily be computed in an online manner. The resulting new generation of learning algorithms for recurrent neural networks provides a new understanding of network learning in the brain that can be tested experimentally. In addition, these algorithms provide efficient methods for on-chip training of RSNNs in neuromorphic hardware.

Includes members:
  Member "synaptic plasticity dynamics for deep continuous local learning decolle":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploiting neuron and synapse filter dynamics in spatial temporal learning of deep spiking neural network")
  Member "synaptic plasticity dynamics for deep continuous local learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploiting neuron and synapse filter dynamics in spatial temporal learning of deep spiking neural network")
  Member "long short term memory and learning to learn in networks of spiking neurons":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a spike in performance training hybrid spiking neural networks with quantized activation functions")
  Member "measuring abstract reasoning in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "making sense of sensory input")
  Member "lstm a search space odyssey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "understanding synthetic gradients and decoupled neural interfaces":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "decoupled greedy learning of cnns")
  Member "deep rewiring training very sparse deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on pruning adversarially robust neural networks")
  Member "rl 2 fast reinforcement learning via slow reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

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)