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 "recent advances in physical reservoir computing a review"):
  Abstract: Abstract   Reservoir computing is a computational framework suited for temporal/sequential data processing. It is derived from several recurrent neural network models, including echo state networks and liquid state machines. A reservoir computing system consists of a reservoir for mapping inputs into a high-dimensional space and a readout for pattern analysis from the high-dimensional states in the reservoir. The reservoir is fixed and only the readout is trained with a simple method such as linear regression and classification. Thus, the major advantage of reservoir computing compared to other recurrent neural networks is fast learning, resulting in low training cost. Another advantage is that the reservoir without adaptive updating is amenable to hardware implementation using a variety of physical systems, substrates, and devices. In fact, such physical reservoir computing has attracted increasing attention in diverse fields of research. The purpose of this review is to provide an overview of recent advances in physical reservoir computing by classifying them according to the type of the reservoir. We discuss the current issues and perspectives related to physical reservoir computing, in order to further expand its practical applications and develop next-generation machine learning systems.

Includes members:
  Member "distributive dynamic spectrum access through deep reinforcement learning a reservoir computing based approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reinforcement learning with convolutional reservoir computing")
  Member "echo state networks are universal":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optical reservoir computing using multiple light scattering for chaotic systems prediction")
  Member "reservoir computing hardware with cellular automata":
    also connected through other hyperedges:
      (none)
  Member "scaling up echo state networks with multiple light scattering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optical reservoir computing using multiple light scattering for chaotic systems prediction")
  Member "reinforcement learning in a large scale photonic recurrent neural network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optical reservoir computing using multiple light scattering for chaotic systems prediction")
  Member "reservoir computing universality with stochastic inputs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "risk bounds for reservoir computing")
  Member "the case for rejecting the memristor as a fundamental circuit element":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an overview of memristive cryptography")
  Member "reservoir computing using non uniform binary cellular automata":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "holographic automata for ambient immersive a i via reservoir 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)