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 "low barrier magnet design for efficient hardware binary stochastic neurons"):
  Abstract: Binary stochastic neurons (BSNs) form an integral part of many machine learning algorithms, motivating the development of hardware accelerators for this complex function. It has been recognized that hardware BSNs can be implemented using low-barrier magnets (LBMs) by minimally modifying present-day magnetoresistive random-access memory (MRAM) devices. A crucial parameter that determines the response of these LBM-based BSN designs is the  correlation time  of magnetization   $\tau _c$  . In this letter, we show that, for magnets with low-energy barriers (  $\Delta \approx k_BT$   and below), circular disk magnets with in-plane magnetic anisotropy (IMA) lead to   $\tau _c$   values that are two orders of magnitude smaller than   $\tau _c$   of magnets with perpendicular magnetic anisotropy (PMA). Analytical descriptions demonstrate that this striking difference in   $\tau _c$   is due to a precessionlike fluctuation mechanism that is enabled by the large demagnetization field in IMA magnets. We provide a detailed energy-delay performance evaluation of previously proposed BSN designs based on spin-orbit torque MRAM and spin-transfer torque MRAM employing low-barrier circular IMA magnets by SPICE simulations. The designs exhibit subnanosecond response times leading to energy requirements of approximately a few femtojoules to evaluate the BSN function, orders of magnitude lower than digital CMOS implementations with a much larger surface area. While modern MRAM technology is based on PMA magnets, results in this letter suggest that low-barrier circular IMA magnets may be more suitable for this application.

Includes members:
  Member "composable probabilistic inference networks using mram based stochastic neurons":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "probabilistic circuits for autonomous learning a simulation study")
  Member "stochastic spiking neural networks enabled by magnetic tunnel junctions from nontelegraphic to telegraphic switching regimes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "p bits for probabilistic spin logic")
  Member "hardware emulation of stochastic p bits for invertible logic":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "p bits for probabilistic spin logic")
  Member "vlsi implementation of deep neural network using integral stochastic computing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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)