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: "demonstrating advantages of neuromorphic computation a pilot study"
  Abstract: Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency. We employ a single-chip prototype of the BrainScaleS 2 neuromorphic system to implement a proof-of-concept demonstration of reward-modulated spike-timing-dependent plasticity in a spiking network that learns to play a simplified version of the Pong video game by smooth pursuit. This system combines an electronic mixed-signal substrate for emulating neuron and synapse dynamics with an embedded digital processor for on-chip learning, which in this work also serves to simulate the virtual environment and learning agent. The analog emulation of neuronal membrane dynamics enables a 1000-fold acceleration with respect to biological real-time, with the entire chip operating on a power budget of 57 mW. Compared to an equivalent simulation using state-of-the-art software, the on-chip emulation is at least one order of magnitude faster and three orders of magnitude more energy-efficient. We demonstrate how on-chip learning can mitigate the effects of fixed-pattern noise, which is unavoidable in analog substrates, while making use of temporal variability for action exploration. Learning compensates imperfections of the physical substrate, as manifested in neuronal parameter variability, by adapting synaptic weights to match respective excitability of individual neurons.

Connected through hyperedge HE-1 (induced by source paper "minimal spiking neuron for solving multi label classification tasks"):
  with members:
    - "surrogate gradient learning in spiking neural networks"
    - "exploring the limits of weakly supervised pretraining"
    - "bp stdp approximating backpropagation using spike timing dependent plasticity"

Connected through hyperedge HE-2 (induced by source paper "fast and deep neuromorphic learning with time to first spike coding"):
  with members:
    - "biologically plausible deep learning but how far can we go with shallow networks"
    - "deep spiking neural network with spike count based learning rule"
    - "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"
    - "deep learning in spiking neural networks"
    - "spinnaker 2 a 10 million core processor system for brain simulation and machine learning"
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "surrogate gradient learning in spiking neural networks"
    - "s4nn temporal backpropagation for spiking neural networks with one spike per neuron"

Connected through hyperedge HE-3 (induced by source paper "exploring deep spiking neural networks for automated driving applications"):
  with members:
    - "deep learning in spiking neural networks"
    - "object detection based on lidar temporal pulses using spiking neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "on the algorithmic power of spiking neural networks"
    - "spiking deep networks with lif neurons"
    - "fast r cnn"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-4 (induced by source paper "neuromorphic hardware learns to learn"):
  with members:
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to reinforcement learn"
    - "transfer learning for speech and language processing"

Connected through hyperedge HE-5 (induced by source paper "simulation of neural function in an artificial hebbian network"):
  with members:
    - "efficient reward based structural plasticity on a spinnaker 2 prototype"
    - "large scale neuromorphic spiking array processors a quest to mimic the brain"
    - "the symbol grounding problem"

Connected through hyperedge HE-6 (induced by source paper "control of criticality and computation in spiking neuromorphic networks with plasticity"):
  with members:
    - "jidt an information theoretic toolkit for studying the dynamics of complex systems"
    - "shared information new insights and problems in decomposing information in complex systems"

Connected through hyperedge HE-7 (induced by source paper "brain inspired hardware for artificial intelligence accelerated learning in a physical model spiking neural network"):
  with members:
    - "a survey of neuromorphic computing and neural networks in hardware"

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)