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 "physics for neuromorphic computing"):
  Abstract: Neuromorphic computing takes inspiration from the brain to create energy efficient hardware for information processing, capable of highly sophisticated tasks. In this article, we make the case that building this new hardware necessitates reinventing electronics. We show that research in physics and material science will be key to create artificial nano-neurons and synapses, to connect them together in huge numbers, to organize them in complex systems, and to compute with them efficiently. We describe how some researchers choose to take inspiration from artificial intelligence to move forward in this direction, whereas others prefer taking inspiration from neuroscience, and we highlight recent striking results obtained with these two approaches. Finally, we discuss the challenges and perspectives in neuromorphic physics, which include developing the algorithms and the hardware hand in hand, making significant advances with small toy systems, as well as building large scale networks.

Includes members:
  Member "in situ learning harnessing intrinsic resistive memory variability through markov chain monte carlo sampling":
    also connected through other hyperedges:
      (none)
  Member "restocnet residual stochastic binary convolutional spiking neural network for memory efficient neuromorphic computing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rmp snns residual membrane potential neuron for enabling deeper high accuracy and low latency spiking neural networks")
  Member "outstanding bit error tolerance of resistive ram based binarized neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards explainable bit error tolerance of resistive ram based binarized neural networks")
  Member "energy and policy considerations for deep learning in nlp":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "neuromorphic computing with multi memristive synapses":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mixed precision deep learning based on computational memory")
  Member "all optical machine learning using diffractive deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "noisy machines understanding noisy neural networks and enhancing robustness to analog hardware errors using distillation")
  Member "biological plausibility and stochasticity in scalable vo2 active memristor neurons":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a caloritronics based mott neuristor")
  Member "computing with networks of nonlinear mechanical oscillators":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "recent advances in physical reservoir computing a review")

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)