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: "trainable hardware for dynamical computing using error backpropagation through physical media"
  Abstract: Neural networks are currently implemented on digital Von Neumann machines, which do not fully leverage their intrinsic parallelism. We demonstrate how to use a novel class of reconfigurable dynamical systems for analogue information processing, mitigating this problem. Our generic hardware platform for dynamic, analogue computing consists of a reciprocal linear dynamical system with nonlinear feedback. Thanks to reciprocity, a ubiquitous property of many physical phenomena like the propagation of light and sound, the error backpropagation-a crucial step for tuning such systems towards a specific task-can happen in hardware. This can potentially speed up the optimization process significantly, offering important benefits for the scalability of neuro-inspired hardware. In this paper, we show, using one experimentally validated and one conceptual example, that such systems may provide a straightforward mechanism for constructing highly scalable, fully dynamical analogue computers.

Connected through hyperedge HE-1 (induced by source paper "towards deep physical reservoir computing through automatic task decomposition and mapping"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "photonic delay systems as machine learning implementations"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "all optical machine learning using diffractive deep neural networks"):
  with members:
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "deep learning enhanced mobile phone microscopy"
    - "principles of neuromorphic photonics"
    - "lensless computational imaging through deep learning"
    - "deep learning microscopy"
    - "deep convolutional neural network for inverse problems in imaging"
    - "a survey on deep learning in medical image analysis"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "towards trainable media using waves for neural network style training"):
  with members:
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "playing atari with deep reinforcement learning"

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.NE(Neural and Evolutionary Computing)