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: "random projections through multiple optical scattering approximating kernels at the speed of light"
  Abstract: Random projections have proven extremely useful in many signal processing and machine learning applications. However, they often require either to store a very large random matrix, or to use a different, structured matrix to reduce the computational and memory costs. Here, we overcome this difficulty by proposing an analog, optical device, that performs the random projections literally at the speed of light without having to store any matrix in memory. This is achieved using the physical properties of multiple coherent scattering of coherent light in random media. We use this device on a simple task of classification with a kernel machine, and we show that, on the MNIST database, the experimental results closely match the theoretical performance of the corresponding kernel. This framework can help make kernel methods practical for applications that have large training sets and/or require real-time prediction. We discuss possible extensions of the method in terms of a class of kernels, speed, memory consumption and different problems.

Connected through hyperedge HE-1 (induced by source paper "optical reservoir computing using multiple light scattering for chaotic systems prediction"):
  with members:
    - "recent advances in physical reservoir computing a review"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "don t take it lightly phasing optical random projections with unknown operators"):
  with members:
    - "holographic phase retrieval and reference design"
    - "localization from incomplete euclidean distance matrix performance analysis for the svd mds approach"
    - "holographic phase retrieval and optimal reference design"
    - "practical sketching algorithms for low rank matrix approximation"
    - "orthogonal random features"
    - "euclidean distance matrices essential theory algorithms and applications"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phase retrieval an overview of recent developments"

Connected through hyperedge HE-3 (induced by source paper "what happens on the edge stays on the edge toward compressive deep learning"):
  with members:
    - "all optical machine learning using diffractive deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "more is less a more complicated network with less inference complexity"
    - "net trim convex pruning of deep neural networks with performance guarantee"
    - "compressive spectral clustering"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-4 (induced by source paper "kernel computations from large scale random features obtained by optical processing units"):
  with members:
    - "optical reservoir computing using multiple light scattering for chaotic systems prediction"
    - "scaling up echo state networks with multiple light scattering"
    - "newma a new method for scalable model free online change point detection"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "fastfood approximate kernel expansions in loglinear time"

Connected through hyperedge HE-5 (induced by source paper "optical proof of work"):
  with members:
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "hijacking bitcoin routing attacks on cryptocurrencies"

Connected through hyperedge HE-6 (induced by source paper "newma a new method for scalable model free online change point detection"):
  with members:
    - "change point detection methods for body worn video"
    - "generative moment matching networks"
    - "scan b statistic for kernel change point detection"
    - "detecting change points in the large scale structure of evolving networks"
    - "fastfood approximate kernel expansions in loglinear time"
    - "a kernel method for the two sample problem"

Connected through hyperedge HE-7 (induced by source paper "quantized compressive k means"):
  with members:
    - "compressive k means"
    - "representation and coding of signal geometry"
    - "random projections for k means clustering"
    - "a kernel method for the two sample problem"

Connected through hyperedge HE-8 (induced by source paper "scaling up echo state networks with multiple light scattering"):
  with members:
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "sketching as a tool for numerical linear algebra"
    - "randomized algorithms for matrices and 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)