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 "spiking neural networks for inference and learning a memristor based design perspective"):
  Abstract: On metrics of density and power efficiency, neuromorphic technologies have the potential to surpass mainstream computing technologies in tasks where real-time functionality, adaptability, and autonomy are essential. While algorithmic advances in neuromorphic computing are proceeding successfully, the potential of memristors to improve neuromorphic computing have not yet born fruit, primarily because they are often used as a drop-in replacement to conventional memory. However, interdisciplinary approaches anchored in machine learning theory suggest that multifactor plasticity rules matching neural and synaptic dynamics to the device capabilities can take better advantage of memristor dynamics and its stochasticity. Furthermore, such plasticity rules generally show much higher performance than that of classical Spike Time Dependent Plasticity (STDP) rules. This chapter reviews the recent development in learning with spiking neural network models and their possible implementation with memristor-based hardware.

Includes members:
  Member "on resistive memories one step row readout technique and sensing circuitry":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "non stationary polar codes for resistive memories")
  Member "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a supervised learning algorithm for multilayer spiking neural networks based on temporal coding toward energy efficient vlsi processor design")
  Member "surrogate gradient learning in spiking neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rmp snns residual membrane potential neuron for enabling deeper high accuracy and low latency spiking neural networks")
  Member "contrastive hebbian learning with random feedback weights":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural and synaptic array transceiver a brain inspired computing framework for embedded learning")
  Member "mitigating asymmetric nonlinear weight update effects in hardware neural network based on analog resistive synapse":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stochastic sparse learning with momentum adaptation for imprecise memristor networks")
  Member "synaptic plasticity dynamics for deep continuous local learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "exploiting neuron and synapse filter dynamics in spatial temporal learning of deep spiking neural network")
  Member "slayer spike layer error reassignment in time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rmp snns residual membrane potential neuron for enabling deeper high accuracy and low latency spiking neural networks")
  Member "deep supervised learning using local errors":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fully decoupled neural network learning using delayed gradients")

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.ET(Emerging Technologies)