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 "energy efficient cmos memristive synapses for mixed signal neuromorphic system on a chip"):
  Abstract: Emerging non-volatile memory (NVM), or memristive, devices promise energy-efficient realization of deep learning, when efficiently integrated with mixed-signal integrated circuits on a CMOS substrate. Even though several algorithmic challenges need to be addressed to turn the vision of memristive Neuromorphic Systems-on-a-Chip (NeuSoCs) into reality, issues at the device and circuit interface need immediate attention from the community. In this work, we perform energy-estimation of a NeuSoC system and predict the desirable circuit and device parameters for energy-efficiency optimization. Also, CMOS synapse circuits based on the concept of CMOS memristor emulator are presented as a system prototyping methodology, while practical memristor devices are being developed and integrated with general-purpose CMOS. The proposed mixed-signal memristive synapse can be designed and fabricated using standard CMOS technologies and open doors to interesting applications in cognitive computing circuits.

Includes members:
  Member "a compact cmos memristor emulator circuit and its applications":
    also connected through other hyperedges:
      (none)
  Member "a survey of neuromorphic computing and neural networks in hardware":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a supervised learning algorithm for multilayer spiking neural networks based on temporal coding toward energy efficient vlsi processor design")
  Member "event driven random back propagation enabling neuromorphic deep learning machines":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a deep unsupervised feature learning spiking neural network with binarized classification layers for emnist classification")
  Member "enabling bio plausible multi level stdp using cmos neurons with dendrites and bistable rrams":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a compact cmos memristor emulator circuit and its applications")
  Member "a cmos spiking neuron for brain inspired neural networks with resistive synapses and in situ learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a deep unsupervised feature learning spiking neural network with binarized classification layers for emnist classification")
  Member "homogeneous spiking neuromorphic system for real world pattern recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a deep unsupervised feature learning spiking neural network with binarized classification layers for emnist classification")
  Member "integration of nanoscale memristor synapses in neuromorphic computing architectures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recent advances in physical reservoir computing a review")

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)