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: "training and operation of an integrated neuromorphic network based on metal oxide memristors"
  Abstract: A transistor-free metal-oxide memristor crossbar with low device variability is realised and trained to perform a simple classification task, opening the way to integrated neuromorphic networks of a complexity comparable to that of the human brain, with high operational speed and manageable power dissipation.

Connected through hyperedge HE-1 (induced by source paper "mixed precision deep learning based on computational memory"):
  with members:
    - "long short term memory networks in memristor crossbar arrays"
    - "neuromorphic computing with multi memristive synapses"
    - "mixed precision in memory computing"
    - "training lstm networks with resistive cross point devices"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "training and inference with integers in deep neural networks"
    - "an analysis of neural language modeling at multiple scales"
    - "an improved evaluation framework for generative adversarial networks"

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 "physics for neuromorphic computing"):
  with members:
    - "in situ learning harnessing intrinsic resistive memory variability through markov chain monte carlo sampling"
    - "restocnet residual stochastic binary convolutional spiking neural network for memory efficient neuromorphic computing"
    - "outstanding bit error tolerance of resistive ram based binarized neural networks"
    - "energy and policy considerations for deep learning in nlp"
    - "neuromorphic computing with multi memristive synapses"
    - "all optical machine learning using diffractive deep neural networks"
    - "biological plausibility and stochasticity in scalable vo2 active memristor neurons"
    - "computing with networks of nonlinear mechanical oscillators"

Connected through hyperedge HE-4 (induced by source paper "memristor hardware friendly reinforcement learning"):
  with members:
    - "parameter space noise for exploration"
    - "proximal policy optimization algorithms"
    - "3d simulation for robot arm control with deep q learning"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "design of a hybrid robot control system using memristor model and ant inspired based information transfer protocols"

Connected through hyperedge HE-5 (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"
    - "memory and information processing in neuromorphic systems"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "adam a method for stochastic optimization"
    - "low precision arithmetic for deep learning"

Connected through hyperedge HE-6 (induced by source paper "in situ learning harnessing intrinsic resistive memory variability through markov chain monte carlo sampling"):
  with members:
    - "energy and policy considerations for deep learning in nlp"
    - "neuromorphic computing with multi memristive synapses"
    - "implementing bayesian networks with embedded stochastic mram"
    - "stochastic spin orbit torque devices as elements for bayesian inference"
    - "training deep convolutional neural networks with resistive cross point devices"

Connected through hyperedge HE-7 (induced by source paper "spiking neural networks for inference and learning a memristor based design perspective"):
  with members:
    - "on resistive memories one step row readout technique and sensing circuitry"
    - "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"
    - "surrogate gradient learning in spiking neural networks"
    - "contrastive hebbian learning with random feedback weights"
    - "mitigating asymmetric nonlinear weight update effects in hardware neural network based on analog resistive synapse"
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "slayer spike layer error reassignment in time"
    - "deep supervised learning using local errors"

Connected through hyperedge HE-8 (induced by source paper "puma a programmable ultra efficient memristor based accelerator for machine learning inference"):
  with members:
    - "vibnn hardware acceleration of bayesian neural networks"
    - "group scissor scaling neuromorphic computing design to large neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "bridging the gap between neural networks and neuromorphic hardware with a neural network compiler"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "trannsformer n eural n etwork transform ation for memristive crossbar based neuromorphic system design"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"

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.ET(Emerging Technologies)