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: "an fpga based massively parallel neuromorphic cortex simulator"
  Abstract: This paper presents a massively parallel and scalable neuromorphic cortex simulator designed for simulating large and structurally connected spiking neural networks, such as complex models of various areas of the cortex. The main novelty of this work is the abstraction of a neuromorphic architecture into clusters represented by minicolumns and hypercolumns, analogously to the fundamental structural units observed in neurobiology. Without this approach, simulating large-scale fully connected networks needs prohibitively large memory to store look-up tables for point-to-point connections. Instead, we use a novel architecture, based on the structural connectivity in the neocortex, such that all the required parameters and connections can be stored in on-chip memory. The cortex simulator can be easily reconfigured for simulating different neural networks without any change in hardware structure by programming the memory. A hierarchical communication scheme allows one neuron to have a fan-out of up to 200k neurons. As a proof-of-concept, an implementation on one Altera Stratix V FPGA was able to simulate 20 million to 2.6 billion leaky-integrate-and-fire (LIF) neurons in real time. We verified the system by emulating a simplified auditory cortex (with 100 million neurons). This cortex simulator achieved a low power dissipation of 1.62 {\mu}W per neuron. With the advent of commercially available FPGA boards, our system offers an accessible and scalable tool for the design, real-time simulation, and analysis of large-scale spiking neural networks.

Connected through hyperedge HE-1 (induced by source paper "efficient reward based structural plasticity on a spinnaker 2 prototype"):
  with members:
    - "a 0 086 mm 2 12 7 pj sop 64k synapse 256 neuron online learning digital spiking neuromorphic processor in 28 nm cmos"
    - "large scale neuromorphic spiking array processors a quest to mimic the brain"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "neuromorphic hardware in the loop training a deep spiking network on the brainscales wafer scale system"
    - "learning both weights and connections for efficient neural networks"
    - "a biological realtime neuromorphic system in 28 nm cmos using low leakage switched capacitor circuits"

Connected through hyperedge HE-2 (induced by source paper "large scale neuromorphic spiking array processors a quest to mimic the brain"):
  with members:
    - "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"
    - "a neuromorphic hardware architecture using the neural engineering framework for pattern recognition"
    - "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-3 (induced by source paper "a scalable method to find the shortest path in a graph with circuits of memristors"):
  with members:
    - "image edge detection based on swarm intelligence using memristive networks"
    - "slime mould memristors"
    - "self organization and solution of shortest path optimization problems with memristive networks"

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)