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: "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.

Connected through hyperedge HE-1 (induced by source paper "advectivenet an eulerian lagrangian fluidic reservoir for point cloud processing"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "point voxel cnn for efficient 3d deep learning"
    - "3d point capsule networks"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "multi resolution 3d convolutional neural networks for object recognition"
    - "splatnet sparse lattice networks for point cloud processing"
    - "adaptive o cnn a patch based deep representation of 3d shapes"

Connected through hyperedge HE-2 (induced by source paper "optical reservoir computing using multiple light scattering for chaotic systems prediction"):
  with members:
    - "a substrate independent framework to characterise reservoir computers"
    - "echo state networks are universal"
    - "scaling up echo state networks with multiple light scattering"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "newma a new method for scalable model free online change point detection"
    - "using a reservoir computer to learn chaotic attractors with applications to chaos synchronization and cryptography"
    - "hardware for machine learning challenges and opportunities"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-3 (induced by source paper "combining machine learning with knowledge based modeling for scalable forecasting and subgrid scale closure of large complex spatiotemporal systems"):
  with members:
    - "efficient design of hardware enabled reservoir computing in fpgas"
    - "hybrid forecasting of chaotic processes using machine learning in conjunction with a knowledge based model"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "atomnet a deep convolutional neural network for bioactivity prediction in structure based drug discovery"

Connected through hyperedge HE-4 (induced by source paper "reservoirs learn to learn"):
  with members:
    - "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"
    - "neuromorphic hardware learns to learn"
    - "on the convergence of adam and beyond"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "learning to reinforcement learn"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "dynamical systems as temporal feature spaces"):
  with members:
    - "echo state networks are universal"
    - "reservoir computing universality with stochastic inputs"
    - "gated orthogonal recurrent units on learning to forget"
    - "universal discrete time reservoir computers with stochastic inputs and linear readouts using non homogeneous state affine systems"
    - "the asymptotic performance of linear echo state neural networks"
    - "nonlinear memory capacity of parallel time delay reservoir computers in the processing of multidimensional signals"
    - "guided self organization of input driven recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "neuromorphic electronic systems for reservoir computing"):
  with members:
    - "computing optimal discrete readout weights in reservoir computing is np hard"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "an online structural plasticity rule for generating better reservoirs"
    - "memory and information processing in neuromorphic systems"
    - "liquid state machine with dendritically enhanced readout for low power neuromorphic vlsi implementations"

Connected through hyperedge HE-7 (induced by source paper "coupled nonlinear delay systems as deep convolutional neural networks"):
  with members:
    - "all optical machine learning using diffractive deep neural networks"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "difference between memory and prediction in linear recurrent networks"
    - "embodiment of learning in electro optical signal processors"
    - "optoelectronic reservoir computing"

Connected through hyperedge HE-8 (induced by source paper "nonlinear systems for unconventional computing"):
  with members:
    - "a critical review of recurrent neural networks for sequence learning"
    - "imagenet large scale visual recognition challenge"
    - "sequence to sequence learning with neural networks"
    - "generating sequences with recurrent neural networks"

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)