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 "composable probabilistic inference networks using mram based stochastic neurons"):
  Abstract: Magnetoresistive random access memory (MRAM) technologies with thermally unstable nanomagnets are leveraged to develop an intrinsic stochastic neuron as a building block for restricted Boltzmann machines (RBMs) to form deep belief networks (DBNs). The embedded MRAM-based neuron is modeled using precise physics equations. The simulation results exhibit the desired sigmoidal relation between the input voltages and probability of the output state. A probabilistic inference network simulator (PIN-Sim) is developed to realize a circuit-level model of an RBM utilizing resistive crossbar arrays along with differential amplifiers to implement the positive and negative weight values. The PIN-Sim is composed of five main blocks to train a DBN, evaluate its accuracy, and measure its power consumption. The MNIST dataset is leveraged to investigate the energy and accuracy tradeoffs of seven distinct network topologies in SPICE using the 14nm HP-FinFET technology library with the nominal voltage of 0.8V, in which an MRAM-based neuron is used as the activation function. The software and hardware level simulations indicate that a 784× 200× 10 topology can achieve less than 5% error rates with ∼400pJ energy consumption. The error rates can be reduced to 2.5% by using a 784× 500× 500× 500× 10 DBN at the cost of ∼10× higher energy consumption and significant area overhead. Finally, the effects of specific hardware-level parameters on power dissipation and accuracy tradeoffs are identified via the developed PIN-Sim framework.

Includes members:
  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 "p bits for probabilistic spin logic")
  Member "stochastic spiking neural networks enabled by magnetic tunnel junctions from nontelegraphic to telegraphic switching regimes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "progressive stochastic binarization of deep networks")
  Member "probabilistic deep spiking neural systems enabled by magnetic tunnel junction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "energy efficient stochastic computing with superparamagnetic tunnel junctions")
  Member "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sbsnn stochastic bits enabled binary spiking neural network with on chip learning for energy efficient neuromorphic computing at the edge")
  Member "training a probabilistic graphical model with resistive switching electronic synapses":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "inherent weight normalization in stochastic neural networks")
  Member "binaryconnect training deep neural networks with binary weights during propagations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sparse weight activation training")
  Member "magnetic tunnel junction mimics stochastic cortical spiking neurons":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "restocnet residual stochastic binary convolutional spiking neural network for memory efficient neuromorphic computing")

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)