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: "p bits for probabilistic spin logic"
  Abstract: We introduce the concept of a probabilistic or p-bit, intermediate between the standard bits of digital electronics and the emerging q-bits of quantum computing. We show that low barrier magnets or LBMs provide a natural physical representation for p-bits and can be built either from perpendicular magnets designed to be close to the in-plane transition or from circular in-plane magnets. Magnetic tunnel junctions (MTJs) built using LBMs as free layers can be combined with standard NMOS transistors to provide three-terminal building blocks for large scale probabilistic circuits that can be designed to perform useful functions. Interestingly, this three-terminal unit looks just like the 1T/MTJ device used in embedded magnetic random access memory technology, with only one difference: the use of an LBM for the MTJ free layer. We hope that the concept of p-bits and p-circuits will help open up new application spaces for this emerging technology. However, a p-bit need not involve an MTJ; any fluctuating resistor could be combined with a transistor to implement it, while completely digital implementations using conventional CMOS technology are also possible. The p-bit also provides a conceptual bridge between two active but disjoint fields of research, namely, stochastic machine learning and quantum computing. First, there are the applications that are based on the similarity of a p-bit to the binary stochastic neuron (BSN), a well-known concept in machine learning. Three-terminal p-bits could provide an efficient hardware accelerator for the BSN. Second, there are the applications that are based on the p-bit being like a poor man's q-bit. Initial demonstrations based on full SPICE simulations show that several optimization problems, including quantum annealing are amenable to p-bit implementations which can be scaled up at room temperature using existing technology.We introduce the concept of a probabilistic or p-bit, intermediate between the standard bits of digital electronics and the emerging q-bits of quantum computing. We show that low barrier magnets or LBMs provide a natural physical representation for p-bits and can be built either from perpendicular magnets designed to be close to the in-plane transition or from circular in-plane magnets. Magnetic tunnel junctions (MTJs) built using LBMs as free layers can be combined with standard NMOS transistors to provide three-terminal building blocks for large scale probabilistic circuits that can be designed to perform useful functions. Interestingly, this three-terminal unit looks just like the 1T/MTJ device used in embedded magnetic random access memory technology, with only one difference: the use of an LBM for the MTJ free layer. We hope that the concept of p-bits and p-circuits will help open up new application spaces for this emerging technology. However, a p-bit need not involve an MTJ; any fluctuating resisto...

Connected through hyperedge HE-1 (induced by source paper "energy efficient stochastic computing with superparamagnetic tunnel junctions"):
  with members:
    - "low barrier magnet design for efficient hardware binary stochastic neurons"
    - "voltage driven building block for hardware belief networks"
    - "implementing bayesian networks with embedded stochastic mram"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "vlsi implementation of deep neural network using integral stochastic computing"
    - "reservoir computing using stochastic p bits"
    - "hardware driven nonlinear activation for stochastic computing based deep convolutional neural networks"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"

Connected through hyperedge HE-2 (induced by source paper "autonomous probabilistic coprocessing with petaflips per second"):
  with members:
    - "composable probabilistic inference networks using mram based stochastic neurons"
    - "scrambled linear pseudorandom number generators"
    - "stochastic spiking neural networks enabled by magnetic tunnel junctions from nontelegraphic to telegraphic switching regimes"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "oscillator based ising machine"
    - "fpga hardware acceleration of monte carlo simulations for the ising model"
    - "janus ii a new generation application driven computer for spin system simulations"

Connected through hyperedge HE-3 (induced by source paper "stochastic magnetoelectric neuron for temporal information encoding"):
  with members:
    - "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"
    - "proposal for an all spin artificial neural network emulating neural and synaptic functionalities through domain wall motion in ferromagnets"
    - "spin orbit torque induced spike timing dependent plasticity"

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)