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 a probabilistic graphical model with resistive switching electronic synapses"
  Abstract: Current large-scale implementations of deep learning and data mining require thousands of processors, massive amounts of off-chip memory, and consume gigajoules of energy. New memory technologies, such as nanoscale two-terminal resistive switching memory devices, offer a compact, scalable, and low-power alternative that permits on-chip colocated processing and memory in fine-grain distributed parallel architecture. Here, we report the first use of resistive memory devices for implementing and training a restricted Boltzmann machine (RBM), a generative probabilistic graphical model as a key component for unsupervised learning in deep networks. We experimentally demonstrate a 45-synapse RBM realized with 90 resistive phase change memory (PCM) elements trained with a bioinspired variant of the contrastive divergence algorithm, implementing Hebbian and anti-Hebbian weight updates. The resistive PCM devices show a twofold to tenfold reduction in error rate in a missing pixel pattern completion task trained over 30 epochs, compared with untrained case. Measured programming energy consumption is 6.1 nJ per epoch with the PCM devices, a factor of ~ 150 times lower than the conventional processor-memory systems. We analyze and discuss the dependence of learning performance on cycle-to-cycle variations and number of gradual levels in the PCM analog memory devices.

Connected through hyperedge HE-1 (induced by source paper "inherent weight normalization in stochastic neural networks"):
  with members:
    - "self normalizing neural networks"
    - "learning discrete weights using the local reparameterization trick"
    - "emnist an extension of mnist to handwritten letters"
    - "learning to learn by gradient descent by gradient descent"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "stochastic synapses enable efficient brain inspired learning machines"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "composable probabilistic inference networks using mram based stochastic neurons"):
  with members:
    - "low energy deep belief networks using intrinsic sigmoidal spintronic based probabilistic neurons"
    - "stochastic spiking neural networks enabled by magnetic tunnel junctions from nontelegraphic to telegraphic switching regimes"
    - "vlsi implementation of deep neural network using integral stochastic computing"
    - "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"
    - "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "magnetic tunnel junction mimics stochastic cortical spiking neurons"

Connected through hyperedge HE-3 (induced by source paper "large scale neuromorphic spiking array processors a quest to mimic the brain"):
  with members:
    - "an fpga based massively parallel neuromorphic cortex simulator"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "stochastic synapses enable efficient brain inspired learning machines"
    - "a neuromorphic hardware architecture using the neural engineering framework for pattern recognition"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "event driven contrastive divergence for spiking neuromorphic systems"
    - "neuromorphic electronic circuits for building autonomous cognitive systems"

Connected through hyperedge HE-4 (induced by source paper "a memristor based optimization framework for ai applications"):
  with members:
    - "algorithm hardware co optimization of the memristor based framework for solving socp and homogeneous qcqp problems"
    - "group scissor scaling neuromorphic computing design to large neural networks"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "optimization with sparsity inducing penalties"
    - "optimal spectrum sharing in mimo cognitive radio networks via semidefinite programming"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-5 (induced by source paper "r dbn a resistive deep belief network architecture leveraging the intrinsic behavior of probabilistic devices"):
  with members:
    - "vlsi implementation of deep neural network using integral stochastic computing"
    - "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"
    - "magnetic tunnel junction mimics stochastic cortical spiking neurons"

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)