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 survey of neuromorphic computing and neural networks in hardware"
  Abstract: Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture. This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems. The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities. In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history. We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications. We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled. The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed.

Connected through hyperedge HE-1 (induced by source paper "a supervised learning algorithm for multilayer spiking neural networks based on temporal coding toward energy efficient vlsi processor design"):
  with members:
    - "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"
    - "deep learning in spiking neural networks"
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "surrogate gradient learning in spiking neural networks"
    - "an energy efficient time domain analog vlsi neural network processor based on a pulse width modulation approach"
    - "supervised learning in spiking neural networks with phase change memory synapses"
    - "a time domain analog weighted sum calculation model for extremely low power vlsi implementation of multi layer neural networks"
    - "multiscale co design analysis of energy latency area and accuracy of a reram analog neural training accelerator"

Connected through hyperedge HE-2 (induced by source paper "neuromemristive circuits for edge computing a review"):
  with members:
    - "long short term memory networks in memristor crossbar arrays"
    - "learning in memristive neural network architectures using analog backpropagation circuits"
    - "neuron inspired data encoding memristive multi level memory cell"
    - "hierarchical temporal memory using memristor networks a survey"
    - "binary weighted memristive analog deep neural network for near sensor edge processing"
    - "design of cmos memristor circuits for lstm architecture"
    - "memristive lstm network hardware architecture for time series predictive modeling problem"
    - "approximate probabilistic neural networks with gated threshold logic"

Connected through hyperedge HE-3 (induced by source paper "design space exploration of hardware spiking neurons for embedded artificial intelligence"):
  with members:
    - "deep learning in spiking neural networks"
    - "spyketorch efficient simulation of convolutional spiking neural networks with at most one spike per neuron"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "supervised learning based on temporal coding in spiking neural networks"
    - "hfirst a temporal approach to object recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "txsim modeling training of deep neural networks on resistive crossbar systems"):
  with members:
    - "training large scale anns on simulated resistive crossbar arrays"
    - "rxnn a framework for evaluating deep neural networks on resistive crossbars"
    - "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"
    - "in datacenter performance analysis of a tensor processing unit"
    - "analog cmos based resistive processing unit for deep neural network training"
    - "training deep convolutional neural networks with resistive cross point devices"
    - "deep learning with limited numerical precision"
    - "deep speech scaling up end to end speech recognition"

Connected through hyperedge HE-5 (induced by source paper "multi objective optimization for size and resilience of spiking neural networks"):
  with members:
    - "stochasticity and robustness in spiking neural networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "exploiting data representation for fault tolerance"
    - "training deep spiking neural networks using backpropagation"
    - "speeding up convolutional neural networks with low rank expansions"

Connected through hyperedge HE-6 (induced by source paper "probabilistic circuits for autonomous learning a simulation study"):
  with members:
    - "low barrier magnet design for efficient hardware binary stochastic neurons"
    - "voltage driven building block for hardware belief networks"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "deep learning in neural networks"

Connected through hyperedge HE-7 (induced by source paper "improving stdp based visual feature learning with whitening"):
  with members:
    - "decorrelated batch normalization"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "bio inspired spiking convolutional neural network using layer wise sparse coding and stdp learning"
    - "bio inspired unsupervised learning of visual features leads to robust invariant object recognition"

Connected through hyperedge HE-8 (induced by source paper "synaptic integration of spatiotemporal features with a dynamic neuromorphic processor"):
  with members:
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "an accelerated analog neuromorphic hardware system emulating nmda and calcium based non linear dendrites"
    - "neuromorphic electronic circuits for building autonomous cognitive systems"

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)