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: "proposal for an all spin artificial neural network emulating neural and synaptic functionalities through domain wall motion in ferromagnets"
  Abstract: Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking the neuron, or the synapse functionality. While memristive devices have been proposed to emulate biological synapses, spintronic devices have proved to be efficient at performing the thresholding operation of the neuron at ultra-low currents. In this work, we propose an All-Spin Artificial Neural Network where a single spintronic device acts as the basic building block of the system. The device offers a direct mapping to synapse and neuron functionalities in the brain while inter-layer network communication is accomplished via CMOS transistors. To the best of our knowledge, this is the first demonstration of a neural architecture where a single nanoelectronic device is able to mimic both neurons and synapses. The ultra-low voltage operation of low resistance magneto-metallic neurons enables the low-voltage operation of the array of spintronic synapses, thereby leading to ultra-low power neural architectures. Device-level simulations, calibrated to experimental results, was used to drive the circuit and system level simulations of the neural network for a standard pattern recognition problem. Simulation studies indicate energy savings by    $\sim\!\! 100\times$   in comparison to a corresponding digital/analog CMOS neuron implementation.

Connected through hyperedge HE-1 (induced by source paper "cmos free multilayer perceptron enabled by four terminal mtj device"):
  with members:
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-2 (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"

Connected through hyperedge HE-3 (induced by source paper "x changr changing memristive crossbar mapping for mitigating line resistance induced accuracy degradation in deep neural networks"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "neuromorphic computing with multi memristive synapses"
    - "rxnn a framework for evaluating deep neural networks on resistive crossbars"
    - "8t sram cell as a multi bit dot product engine for beyond von neumann computing"
    - "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"
    - "xcel ram accelerating binary neural networks in high throughput sram compute arrays"
    - "group scissor scaling neuromorphic computing design to large neural networks"
    - "trannsformer n eural n etwork transform ation for memristive crossbar based neuromorphic system design"

Connected through hyperedge HE-4 (induced by source paper "all spin bayesian neural networks"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "implementing bayesian networks with embedded stochastic mram"
    - "vibnn hardware acceleration of bayesian neural networks"
    - "stochastic spin orbit torque devices as elements for bayesian inference"
    - "encoding neural and synaptic functionalities in electron spin a pathway to efficient neuromorphic computing"
    - "resparc a reconfigurable and energy efficient architecture with memristive crossbars for deep spiking neural networks"
    - "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"
    - "vime variational information maximizing exploration"

Connected through hyperedge HE-5 (induced by source paper "stochastic magnetoelectric neuron for temporal information encoding"):
  with members:
    - "p bits for probabilistic spin logic"
    - "design of a low voltage analog to digital converter using voltage controlled stochastic switching of low barrier nanomagnets"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "energy efficient memories using magneto electric switching of ferromagnets"
    - "mesl proposal for a non volatile cascadable magneto electric spin logic"
    - "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"
    - "spin orbit torque induced spike timing dependent plasticity"

Connected through hyperedge HE-6 (induced by source paper "rxnn a framework for evaluating deep neural networks on resistive crossbars"):
  with members:
    - "group scissor scaling neuromorphic computing design to large neural networks"
    - "analog cmos based resistive processing unit for deep neural network training"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "training deep convolutional neural networks with resistive cross point devices"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"
    - "stt snn a spin transfer torque based soft limiting non linear neuron for low power artificial neural networks"
    - "deep learning with limited numerical precision"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-7 (induced by source paper "design of a low voltage analog to digital converter using voltage controlled stochastic switching of low barrier nanomagnets"):
  with members:
    - "mesl proposal for a non volatile cascadable magneto electric spin logic"
    - "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"
    - "magnetic tunnel junction mimics stochastic cortical spiking neurons"

Connected through hyperedge HE-8 (induced by source paper "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"):
  with members:
    - "deep learning in neural networks"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "brain like associative learning using a nanoscale non volatile phase change synaptic device array"

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)