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 "a survey of neuromorphic computing and neural networks in hardware"):
  Abstract: Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture. This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems. The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities. In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history. We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications. We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled. The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed.

Includes members:
  Member "a study of complex deep learning networks on high performance neuromorphic and quantum computers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "support vector machines on the d wave quantum annealer")
  Member "probabilistic deep spiking neural systems enabled by magnetic tunnel junction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "energy efficient stochastic computing with superparamagnetic tunnel junctions")
  Member "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sbsnn stochastic bits enabled binary spiking neural network with on chip learning for energy efficient neuromorphic computing at the edge")
  Member "unsupervised learning in neuromemristive systems":
    also connected through other hyperedges:
      (none)
  Member "a nonparametric framework for quantifying generative inference on neuromorphic systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a design methodology for efficient implementation of deconvolutional neural networks on an fpga")
  Member "short term plasticity and long term potentiation in magnetic tunnel junctions towards volatile synapses":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "encoding neural and synaptic functionalities in electron spin a pathway to efficient neuromorphic computing")
  Member "a new learning method for inference accuracy core occupation and performance co optimization on truenorth chip":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "vibnn hardware acceleration of bayesian neural networks")
  Member "exploiting the short term to long term plasticity transition in memristive nanodevice learning architectures":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "spatio temporal learning with arrays of analog nanosynapses")

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)