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 "energy efficient stochastic computing with superparamagnetic tunnel junctions"):
  Abstract: Superparamagnetic tunnel junctions have emerged as a competitive, realistic nanotechnology to support novel forms of stochastic computation in CMOS-compatible platforms. One of their applications is to generate random bitstreams suitable for use in stochastic computing implementations. We describe a method for digitally programmable bitstream generation based on pre-charge sense amplifiers which is more energy efficient than previously explored alternatives. The energy savings offered by this digital generator survive when we use them as the fundamental units of a neural network architecture. To take advantage of the potential savings, we codesign the algorithm with the circuit, rather than directly transcribing a classical neural network into hardware. The flexibility of the neural network mathematics compensates for explicitly energy efficient choices we make at the device level. The result is a convolutional neural network design operating at $\approx 150~nJ$ per inference with $97~\%$ performance on MNIST---nearly an order of magnitude more energy efficiency than comparable proposals in the recent literature.

Includes members:
  Member "low barrier magnet design for efficient hardware binary stochastic neurons":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "probabilistic circuits for autonomous learning a simulation study")
  Member "p bits for probabilistic spin logic":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "autonomous probabilistic coprocessing with petaflips per second")
  Member "voltage driven building block for hardware belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "probabilistic circuits for autonomous learning a simulation study")
  Member "implementing bayesian networks with embedded stochastic mram":
    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 "efficient processing of deep neural networks a tutorial and survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "searching for winograd aware quantized networks")
  Member "vlsi implementation of deep neural network using integral stochastic computing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "progressive stochastic binarization of deep networks")
  Member "reservoir computing using stochastic p bits":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hardware based spatio temporal neural processing backend for imaging sensors towards a smart camera")
  Member "hardware driven nonlinear activation for stochastic computing based deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "design space exploration of neural network activation function circuits")

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)