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: "towards trainable media using waves for neural network style training"
  Abstract: In this paper we study the concept of using the interaction between waves and a trainable medium in order to construct a matrix-vector multiplier. In particular we study such a device in the context of the backpropagation algorithm, which is commonly used for training neural networks. Here, the weights of the connections between neurons are trained by multiplying a `forward' signal with a backwards propagating `error' signal. We show that this concept can be extended to trainable media, where the gradient for the local wave number is given by multiplying signal waves and error waves. We provide a numerical example of such a system with waves traveling freely in a trainable medium, and we discuss a potential way to build such a device in an integrated photonics chip.

Connected through hyperedge HE-1 (induced by source paper "asp vision optically computing the first layer of convolutional neural networks using angle sensitive pixels"):
  with members:
    - "convolutional sparse coding for high dynamic range imaging"
    - "deep fully connected networks for video compressive sensing"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "neural networks with few multiplications"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "reconstruction free action inference from compressive imagers"
    - "compressing convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "acdc a structured efficient linear layer"):
  with members:
    - "random projections through multiple optical scattering approximating kernels at the speed of light"
    - "end to end memory networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "tensorizing neural networks"
    - "learning both weights and connections for efficient neural networks"
    - "speeding up neural networks for large scale classification using wta hashing"
    - "compressing neural networks with the hashing trick"
    - "an exploration of parameter redundancy in deep networks with circulant projections"

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)