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: "memory and information processing in neuromorphic systems"
  Abstract: A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.

Connected through hyperedge HE-1 (induced by source paper "precise neural network computation with imprecise analog devices"):
  with members:
    - "binareye an always on energy accuracy scalable binary cnn processor with all memory on chip in 28nm cmos"
    - "a 128 channel extreme learning machine based neural decoder for brain machine interfaces"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "adam a method for stochastic optimization"
    - "low precision arithmetic for deep learning"

Connected through hyperedge HE-2 (induced by source paper "is my neural network neuromorphic taxonomy recent trends and future directions in neuromorphic engineering"):
  with members:
    - "spiking neural network based region proposal networks for neuromorphic vision sensors"
    - "ebbiot a low complexity tracking algorithm for surveillance in iovt using stationary neuromorphic vision sensors"
    - "real time closed loop neural decoding on a neuromorphic chip"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "a 128 channel extreme learning machine based neural decoder for brain machine interfaces"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "memristive properties of mushrooms"):
  with members:
    - "on resistance switching and oscillations in tubulin microtubule droplets"
    - "fungal architecture"

Connected through hyperedge HE-4 (induced by source paper "benchmarking physical performance of neural inference circuits"):
  with members:
    - "enabling spike based backpropagation in state of the art deep neural network architectures"
    - "large scale neuromorphic spiking array processors a quest to mimic the brain"
    - "multiscale co design analysis of energy latency area and accuracy of a reram analog neural training accelerator"
    - "benchmarking inverse rashba edelstein magnetoelectric devices for neuromorphic computing"
    - "benchmarking keyword spotting efficiency on neuromorphic hardware"
    - "an analysis of deep neural network models for practical applications"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "a survey of neuromorphic computing and neural networks in hardware"

Connected through hyperedge HE-5 (induced by source paper "harnessing slow dynamics in neuromorphic computation"):
  with members:
    - "surrogate gradient learning in spiking neural networks"
    - "analog circuits for mixed signal neuromorphic computing architectures in 28 nm fd soi technology"
    - "real time closed loop neural decoding on a neuromorphic chip"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "slayer spike layer error reassignment in time"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "trained ternary quantization"
    - "convolutional networks for fast energy efficient neuromorphic computing"

Connected through hyperedge HE-6 (induced by source paper "digital biologically plausible implementation of binarized neural networks with differential hafnium oxide resistive memory arrays"):
  with members:
    - "nvidia tensor core programmability performance precision"
    - "in memory and error immune differential rram implementation of binarized deep neural networks"
    - "towards accurate binary convolutional neural network"
    - "dark memory and accelerator rich system optimization in the dark silicon era"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

Connected through hyperedge HE-7 (induced by source paper "a 0 086 mm 2 12 7 pj sop 64k synapse 256 neuron online learning digital spiking neuromorphic processor in 28 nm cmos"):
  with members:
    - "minimum energy quantized neural networks"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "an ultralow leakage synaptic scaling homeostatic plasticity circuit with configurable time scales up to 100 ks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "a biological realtime neuromorphic system in 28 nm cmos using low leakage switched capacitor circuits"
    - "switched capacitor realization of presynaptic short term plasticity and stop learning synapses in 28 nm cmos"
    - "biologically inspired spiking neurons piecewise linear models and digital implementation"

Connected through hyperedge HE-8 (induced by source paper "neuromorphic electronic systems for reservoir computing"):
  with members:
    - "recent advances in physical reservoir computing a review"
    - "computing optimal discrete readout weights in reservoir computing is np hard"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "an online structural plasticity rule for generating better reservoirs"
    - "liquid state machine with dendritically enhanced readout for low power neuromorphic vlsi implementations"

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)