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 "mad mapping and debugging framework for implementing deep neural network onto a neuromorphic chip with crossbar array of synapses"):
  Abstract: Neuromorphic systems or dedicated hardware for neuromorphic computing is getting popular with the advancement in research on different device materials for synapses, especially in crossbar architecture and also algorithms specific or compatible to neuromorphic hardware. Hence, an automated mapping of any deep neural network onto the neuromorphic chip with crossbar array of synapses and an efficient debugging framework is very essential. Here, mapping is defined as the deployment of a section of deep neural network layer onto a neuromorphic core and the generation of connection lists among population of neurons to specify the connectivity between various neuromorphic cores on the neuromorphic chip. Debugging is the verification of computations performed on the neuromorphic chip during inferencing. Together the framework becomes Mapping and Debugging (MaD) framework. MaD framework is quite general in usage as it is a Python wrapper which can be integrated with almost every simulator tools for neuromorphic chips. This paper illustrates the MaD framework in detail, considering some optimizations while mapping onto a single neuromorphic core. A classification task on MNIST and CIFAR-10 datasets are considered for test case implementation of MaD framework.

Includes members:
  Member "deep versus wide convolutional neural networks for object recognition on neuromorphic system":
    also connected through other hyperedges:
      (none)
  Member "structured convolution matrices for energy efficient deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hardware friendly neural network architecture for neuromorphic computing")
  Member "training and operation of an integrated neuromorphic network based on metal oxide memristors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mixed precision deep learning based on computational memory")

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)