human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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...

Includes members:
  Member "weighted p bits for fpga implementation of probabilistic circuits":
    also connected through other hyperedges:
      (none)
  Member "low energy deep belief networks using intrinsic sigmoidal spintronic based probabilistic neurons":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "composable probabilistic inference networks using mram based stochastic neurons")
  Member "voltage driven building block for hardware belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic circuits for autonomous learning a simulation study")
  Member "perspective memcomputing leveraging memory and physics to compute efficiently":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "aircraft loading optimization memcomputing the 5th airbus problem")
  Member "stochastic spin orbit torque devices as elements for bayesian inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "in situ learning harnessing intrinsic resistive memory variability through markov chain monte carlo sampling")
  Member "stochastic spiking neural networks enabled by magnetic tunnel junctions from nontelegraphic to telegraphic switching regimes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "autonomous probabilistic coprocessing with petaflips per second")
  Member "hardware emulation of stochastic p bits for invertible logic":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "low barrier magnet design for efficient hardware binary stochastic neurons")
  Member "vlsi implementation of deep neural network using integral stochastic computing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "progressive stochastic binarization of deep networks")

Question: Please tell me which class the source paper 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)