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: "principles of neuromorphic photonics"
  Abstract: In an age overrun with information, the ability to process reams of data has become crucial. The demand for data will continue to grow as smart gadgets multiply and become increasingly integrated into our daily lives. Next-generation industries in artificial intelligence services and high-performance computing are so far supported by microelectronic platforms. These data-intensive enterprises rely on continual improvements in hardware. Their prospects are running up against a stark reality: conventional one-size-fits-all solutions offered by digital electronics can no longer satisfy this need, as Moore's law (exponential hardware scaling), interconnection density, and the von Neumann architecture reach their limits. #R##N#With its superior speed and reconfigurability, analog photonics can provide some relief to these problems; however, complex applications of analog photonics have remained largely unexplored due to the absence of a robust photonic integration industry. Recently, the landscape for commercially-manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. #R##N#The scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of \emph{neuromorphic photonics}. This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.

Connected through hyperedge HE-1 (induced by source paper "analysis of diffractive optical neural networks and their integration with electronic neural networks"):
  with members:
    - "on chip optical convolutional neural networks"
    - "all optical machine learning using diffractive deep neural networks"
    - "pcnna a photonic convolutional neural network accelerator"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "toward fast neural computing using all photonic phase change spiking neurons"
    - "be careful what you backpropagate a case for linear output activations gradient boosting"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-2 (induced by source paper "design of task specific optical systems using broadband diffractive neural networks"):
  with members:
    - "analysis of diffractive optical neural networks and their integration with electronic neural networks"
    - "class specific differential detection in diffractive optical neural networks improves inference accuracy"
    - "virtual histological staining of unlabelled tissue autofluorescence images via deep learning"
    - "all optical machine learning using diffractive deep neural networks"
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "deep learning enhanced mobile phone microscopy"
    - "lensless computational imaging through deep learning"
    - "deep learning microscopy"

Connected through hyperedge HE-3 (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"
    - "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"
    - "learning a variational network for reconstruction of accelerated mri data"

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)