human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "spiking neural algorithms for markov process random walk"
  Abstract: The random walk is a fundamental stochastic process that underlies many numerical tasks in scientific computing applications. We consider here two neural algorithms that can be used to efficiently implement random walks on spiking neuromorphic hardware. The first method tracks the positions of individual walkers independently by using a modular code inspired by the grid cell spatial representation in the brain. The second method tracks the densities of random walkers at each spatial location directly. We analyze the scaling complexity of each of these methods and illustrate their ability to model random walkers under different probabilistic conditions.

Connected through hyperedge HE-1 (induced by source paper "spike based primitives for graph algorithms"):
  with members:
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "community detection with spiking neural networks for neuromorphic hardware"
    - "mathematical foundations of the graphblas"
    - "memory and information processing in neuromorphic systems"
    - "standards for graph algorithm primitives"

Connected through hyperedge HE-2 (induced by source paper "composing neural algorithms with fugu"):
  with members:
    - "training deep neural networks for binary communication with the whetstone method"
    - "spike based primitives for graph algorithms"

Connected through hyperedge HE-3 (induced by source paper "making bread biomimetic strategies for artificial intelligence now and in the future"):
  with members:
    - "training deep neural networks for binary communication with the whetstone method"
    - "in datacenter performance analysis of a tensor processing unit"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "training spiking deep networks for neuromorphic hardware"
    - "stick spike time interval computational kernel a framework for general purpose computation using neurons precise timing delays and synchrony"

Question: Please tell me which class the center node 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)