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: "design of a low voltage analog to digital converter using voltage controlled stochastic switching of low barrier nanomagnets"
  Abstract: Their inherent stochasticity makes nanoscale devices prospective candidates for low-power computations. Such devices have been demonstrated to exhibit probabilistic switching between two stable states to achieve stochastic behavior. Recently, superparamagnetic nanomagnets (having low-energy barrier    $E_B \approx 1\,kT$  ) have shown promise of achieving stochastic switching at gigahertz rates, with very low currents. On the other hand, voltage-controlled switching of nanomagnets through the magneto-electric effect has shown further improvements in energy efficiency. In this letter, we model a voltage-controlled spintronic device based on superparamagnetic nanomagnets exhibiting telegraphic switching characteristics and analyze its behavior under the influence of external bias. Subsequently, we show that the device leverages the voltage-controlled stochasticity in performing low-voltage 8 bit analog-to-digital conversions. This eliminates the need for comparators, unlike the CMOS-based Flash analog-to-digital converters (ADCs). This device allows for a simple and compact design of low-power approximate ADCs, which have become quintessential elements with the recent developments in neuromorphic computing and “Internet of Things.”

Connected through hyperedge HE-1 (induced by source paper "stochastic magnetoelectric neuron for temporal information encoding"):
  with members:
    - "p bits for probabilistic spin logic"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "energy efficient memories using magneto electric switching of ferromagnets"
    - "mesl proposal for a non volatile cascadable magneto electric spin logic"
    - "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"
    - "proposal for an all spin artificial neural network emulating neural and synaptic functionalities through domain wall motion in ferromagnets"
    - "spin orbit torque induced spike timing dependent plasticity"

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)