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: "a neuromorphic hardware architecture using the neural engineering framework for pattern recognition"
  Abstract: We present a hardware architecture that uses the Neural Engineering Framework (NEF) to implement large-scale neural networks on Field Programmable Gate Arrays (FPGAs) for performing pattern recognition in real time. NEF is a framework that is capable of synthesising large-scale cognitive systems from subnetworks. We will first present the architecture of the proposed neural network implemented using fixed-point numbers and demonstrate a routine that computes the decoding weights by using the online pseudoinverse update method (OPIUM) in a parallel and distributed manner. The proposed system is efficiently implemented on a compact digital neural core. This neural core consists of 64 neurons that are instantiated by a single physical neuron using a time-multiplexing approach. As a proof of concept, we combined 128 identical neural cores together to build a handwritten digit recognition system using the MNIST database and achieved a recognition rate of 96.55%. The system is implemented on a state-of-the-art FPGA and can process 5.12 million digits per second. The architecture is not limited to handwriting recognition, but is generally applicable as an extremely fast pattern recognition processor for various kinds of patterns such as speech and images.

Connected through hyperedge HE-1 (induced by source paper "large scale neuromorphic spiking array processors a quest to mimic the brain"):
  with members:
    - "an fpga based massively parallel neuromorphic cortex simulator"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "training a probabilistic graphical model with resistive switching electronic synapses"
    - "stochastic synapses enable efficient brain inspired learning machines"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "event driven contrastive divergence for spiking neuromorphic systems"
    - "neuromorphic electronic circuits for building autonomous cognitive systems"

Connected through hyperedge HE-2 (induced by source paper "unsupervised learning in synaptic sampling machines"):
  with members:
    - "spiking deep networks with lif neurons"
    - "real time unsupervised learning of visual stimuli in neuromorphic vlsi systems"
    - "neural network regularization via robust weight factorization"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (induced by source paper "an online learning algorithm for neuromorphic hardware implementation"):
  with members:
    - "a neuromorphic hardware framework based on population coding"
    - "fpga implementation of the car model of the cochlea"
    - "2013 special issue learning the pseudoinverse solution to network weights"

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)