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 "all spin bayesian neural networks"):
  Abstract: Probabilistic machine learning enabled by the Bayesian formulation has recently gained significant attention in the domain of automated reasoning and decision-making. While impressive strides have been made recently to scale up the performance of deep Bayesian neural networks, they have been primarily standalone software efforts without any regard to the underlying hardware implementation. In this paper, we propose an "All-Spin" Bayesian Neural Network where the underlying spintronic hardware provides a better match to the Bayesian computing models. To the best of our knowledge, this is the first exploration of a Bayesian neural hardware accelerator enabled by emerging post-CMOS technologies. We develop an experimentally calibrated device-circuit-algorithm co-simulation framework and demonstrate $23.6\times$ reduction in energy consumption against an iso-network CMOS baseline implementation.

Includes members:
  Member "puma a programmable ultra efficient memristor based accelerator for machine learning inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compiling neural networks for a computational memory accelerator")
  Member "implementing bayesian networks with embedded stochastic mram":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "in situ learning harnessing intrinsic resistive memory variability through markov chain monte carlo sampling")
  Member "vibnn hardware acceleration of bayesian neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploiting oxide based resistive ram variability for bayesian neural network hardware design")
  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 "encoding neural and synaptic functionalities in electron spin a pathway to efficient neuromorphic computing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards scalable efficient and accurate deep spiking neural networks with backward residual connections stochastic softmax and hybridization")
  Member "resparc a reconfigurable and energy efficient architecture with memristive crossbars for deep spiking neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reinforcement co learning of deep and spiking neural networks for energy efficient mapless navigation with neuromorphic hardware")
  Member "probabilistic deep spiking neural systems enabled by magnetic tunnel junction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "energy efficient stochastic computing with superparamagnetic tunnel junctions")
  Member "proposal for an all spin artificial neural network emulating neural and synaptic functionalities through domain wall motion in ferromagnets":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cmos free multilayer perceptron enabled by four terminal mtj device")

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)