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: "stick spike time interval computational kernel a framework for general purpose computation using neurons precise timing delays and synchrony"
  Abstract: There has been significant research over the past two decades in developing new platforms for spiking neural computation. Current neural computers are primarily developed to mimick biology. They use neural networks which can be trained to perform specific tasks to mainly solve pattern recognition problems. These machines can do more than simulate biology, they allow us to re-think our current paradigm of computation. The ultimate goal is to develop brain inspired general purpose computation architectures that can breach the current bottleneck introduced by the Von Neumann architecture. This work proposes a new framework for such a machine. We show that the use of neuron like units with precise timing representation, synaptic diversity, and temporal delays allows us to set a complete, scalable compact computation framework. The presented framework provides both linear and non linear operations, allowing us to represent and solve any function. We show usability in solving real use cases from simple differential equations to sets of non-linear differential equations leading to chaotic attractors.

Connected through hyperedge HE-1 (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"
    - "spiking neural algorithms for markov process random walk"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a sparse coding multi scale precise timing machine learning algorithm for neuromorphic event based sensors"):
  with members:
    - "hfirst a temporal approach to object recognition"

Connected through hyperedge HE-3 (induced by source paper "neural and synaptic array transceiver a brain inspired computing framework for embedded learning"):
  with members:
    - "contrastive hebbian learning with random feedback weights"
    - "membrane dependent neuromorphic learning rule for unsupervised spike pattern detection"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "learning in the machine random backpropagation and the deep learning channel"
    - "forward table based presynaptic event triggered spike timing dependent plasticity"
    - "stochastic synapses enable efficient brain inspired learning machines"
    - "supervised learning based on temporal coding in spiking neural networks"
    - "convolutional networks for fast energy efficient neuromorphic computing"

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)