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: "resparc a reconfigurable and energy efficient architecture with memristive crossbars for deep spiking neural networks"
  Abstract: Neuromorphic computing using post-CMOS technologies is gaining immense popularity due to its promising abilities to address the memory and power bottlenecks in von-Neumann computing systems. In this paper, we propose RESPARC - a reconfigurable and energy efficient architecture built-on Memristive Crossbar Arrays (MCA) for deep Spiking Neural Networks (SNNs). Prior works were primarily focused on device and circuit implementations of SNNs on crossbars. RESPARC advances this by proposing a complete system for SNN acceleration and its subsequent analysis. RESPARC utilizes the energy-efficiency of MCAs for inner-product computation and realizes a hierarchical reconfigurable design to incorporate the data-flow patterns in an SNN in a scalable fashion. We evaluate the proposed architecture on different SNNs ranging in complexity from 2k-230k neurons and 1.2M-5.5M synapses. Simulation results on these networks show that compared to the baseline digital CMOS architecture, RESPARC achieves 500X (15X) efficiency in energy benefits at 300X (60X) higher throughput for multi-layer perceptrons (deep convolutional networks). Furthermore, RESPARC is a technology-aware architecture that maps a given SNN topology to the most optimized MCA size for the given crossbar technology.

Connected through hyperedge HE-1 (induced by source paper "reinforcement co learning of deep and spiking neural networks for energy efficient mapless navigation with neuromorphic hardware"):
  with members:
    - "spiking neural network on neuromorphic hardware for energy efficient unidimensional slam"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "slayer spike layer error reassignment in time"
    - "parameter space noise for exploration"
    - "reverse curriculum generation for reinforcement learning"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "conversion of artificial recurrent neural networks to spiking neural networks for low power neuromorphic hardware"

Connected through hyperedge HE-2 (induced by source paper "towards scalable efficient and accurate deep spiking neural networks with backward residual connections stochastic softmax and hybridization"):
  with members:
    - "restocnet residual stochastic binary convolutional spiking neural network for memory efficient neuromorphic computing"
    - "surrogate gradient learning in spiking neural networks"
    - "training deep neural networks for binary communication with the whetstone method"
    - "enabling spike based backpropagation in state of the art deep neural network architectures"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "encoding neural and synaptic functionalities in electron spin a pathway to efficient neuromorphic computing"

Connected through hyperedge HE-3 (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-4 (induced by source paper "restocnet residual stochastic binary convolutional spiking neural network for memory efficient neuromorphic computing"):
  with members:
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "spiking deep residual network"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "unsupervised regenerative learning of hierarchical features in spiking deep networks for object recognition"
    - "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems"
    - "supervised learning based on temporal coding in spiking neural networks"

Connected through hyperedge HE-5 (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"
    - "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"
    - "proposal for an all spin artificial neural network emulating neural and synaptic functionalities through domain wall motion in ferromagnets"
    - "vime variational information maximizing exploration"

Connected through hyperedge HE-6 (induced by source paper "recent advances in convolutional neural network acceleration"):
  with members:
    - "learning efficient convolutional networks through network slimming"
    - "structured probabilistic pruning for convolutional neural network acceleration"
    - "chain nn an energy efficient 1d chain architecture for accelerating deep convolutional neural networks"
    - "mec memory efficient convolution for deep neural network"
    - "coordinating filters for faster deep neural networks"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "channel pruning for accelerating very deep neural networks"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"

Connected through hyperedge HE-7 (induced by source paper "towards accurate and high speed spiking neuromorphic systems with data quantization aware deep networks"):
  with members:
    - "group scissor scaling neuromorphic computing design to large neural networks"
    - "classification accuracy improvement for neuromorphic computing systems with one level precision synapses"
    - "hardware software codesign of accurate multiplier free deep neural networks"
    - "hardware oriented approximation of convolutional neural networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-8 (induced by source paper "xcel ram accelerating binary neural networks in high throughput sram compute arrays"):
  with members:
    - "8t sram cell as a multi bit dot product engine for beyond von neumann computing"
    - "computing in memory with spin transfer torque magnetic ram"
    - "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"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

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)