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: "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.

Connected through hyperedge HE-1 (induced by source paper "autonomous probabilistic coprocessing with petaflips per second"):
  with members:
    - "p bits for probabilistic spin logic"
    - "scrambled linear pseudorandom number generators"
    - "stochastic spiking neural networks enabled by magnetic tunnel junctions from nontelegraphic to telegraphic switching regimes"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "oscillator based ising machine"
    - "fpga hardware acceleration of monte carlo simulations for the ising model"
    - "janus ii a new generation application driven computer for spin system simulations"

Connected through hyperedge HE-2 (induced by source paper "low barrier magnet design for efficient hardware binary stochastic neurons"):
  with members:
    - "voltage driven building block for hardware belief networks"
    - "stochastic spiking neural networks enabled by magnetic tunnel junctions from nontelegraphic to telegraphic switching regimes"
    - "hardware emulation of stochastic p bits for invertible logic"
    - "vlsi implementation of deep neural network using integral stochastic computing"

Connected through hyperedge HE-3 (induced by source paper "adaptive non uniform compressive sensing using sot mram multibit crossbar arrays"):
  with members:
    - "aqurate mram based stochastic oscillator for adaptive quantization rate sampling of sparse signals"

Connected through hyperedge HE-4 (induced by source paper "aqurate mram based stochastic oscillator for adaptive quantization rate sampling of sparse signals"):
  with members:
    - "low energy deep belief networks using intrinsic sigmoidal spintronic based probabilistic neurons"

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)