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: "hardware emulation of stochastic p bits for invertible logic"
  Abstract: The common feature of nearly all logic and memory devices is that they make use of stable units to represent 0’s and 1’s. A completely different paradigm is based on three-terminal stochastic units which could be called “p-bits”, where the output is a random telegraphic signal continuously fluctuating between 0 and 1 with a tunable mean. p-bits can be interconnected to receive weighted contributions from others in a network, and these weighted contributions can be chosen to not only solve problems of optimization and inference but also to implement precise Boolean functions in an inverted mode. This inverted operation of Boolean gates is particularly striking: They provide inputs consistent to a given output along with unique outputs to a given set of inputs. The existing demonstrations of accurate invertible logic are intriguing, but will these striking properties observed in computer simulations carry over to hardware implementations? This paper uses individual micro controllers to emulate p-bits, and we present results for a 4-bit ripple carry adder with 48 p-bits and a 4-bit multiplier with 46 p-bits working in inverted mode as a factorizer. Our results constitute a first step towards implementing p-bits with nano devices, like stochastic Magnetic Tunnel Junctions.

Connected through hyperedge HE-1 (induced by source paper "p bits for probabilistic spin logic"):
  with members:
    - "weighted p bits for fpga implementation of probabilistic circuits"
    - "low energy deep belief networks using intrinsic sigmoidal spintronic based probabilistic neurons"
    - "voltage driven building block for hardware belief networks"
    - "perspective memcomputing leveraging memory and physics to compute efficiently"
    - "stochastic spin orbit torque devices as elements for bayesian inference"
    - "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"
    - "polynomial time solution of prime factorization and np complete problems with digital memcomputing machines"

Connected through hyperedge HE-2 (induced by source paper "low barrier magnet design for efficient hardware binary stochastic neurons"):
  with members:
    - "composable probabilistic inference networks using mram based stochastic neurons"
    - "voltage driven building block for hardware belief networks"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "implementing bayesian networks with embedded stochastic mram"):
  with members:
    - "stochastic spin orbit torque devices as elements for bayesian inference"

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)