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: "deep learning by scattering"
  Abstract: We introduce general scattering transforms as mathematical models of deep neural networks with l2 pooling. Scattering networks iteratively apply complex valued unitary operators, and the pooling is performed by a complex modulus. An expected scattering defines a contractive representation of a high-dimensional probability distribution, which preserves its mean-square norm. We show that unsupervised learning can be casted as an optimization of the space contraction to preserve the volume occupied by unlabeled examples, at each layer of the network. Supervised learning and classification are performed with an averaged scattering, which provides scattering estimations for multiple classes.

Connected through hyperedge HE-1 (induced by source paper "learning to compress and search visual data in large scale systems"):
  with members:
    - "rendition reclaiming what a black box takes away"
    - "polysemous codes"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "billion scale similarity search with gpus"
    - "hashnet deep learning to hash by continuation"
    - "memnet a persistent memory network for image restoration"
    - "full resolution image compression with recurrent neural networks"
    - "end to end optimized image compression"

Connected through hyperedge HE-2 (induced by source paper "learning non parametric invariances from data with permanent random connectomes"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "autoaugment learning augmentation policies from data"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "deep rotation equivariant network"
    - "graph based isometry invariant representation learning"
    - "learning to extract motion from videos in convolutional neural networks"
    - "aggregated residual transformations for deep neural networks"
    - "warped convolutions efficient invariance to spatial transformations"

Connected through hyperedge HE-3 (induced by source paper "building a regular decision boundary with deep networks"):
  with members:
    - "residual networks of residual networks multilevel residual networks"
    - "sgdr stochastic gradient descent with restarts"
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "wide residual networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "manitest are classifiers really invariant"

Connected through hyperedge HE-4 (induced by source paper "deep haar scattering networks"):
  with members:
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "unsupervised deep haar scattering on graphs"
    - "deep roto translation scattering for object classification"
    - "provable bounds for learning some deep representations"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "unsupervised learning of invariant representations in hierarchical architectures"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "deep generative stochastic networks trainable by backprop"

Connected through hyperedge HE-5 (induced by source paper "a deep representation for invariance and music classification"):
  with members:
    - "deep scattering spectrum"
    - "unsupervised learning of invariant representations in hierarchical architectures"
    - "gurls a least squares library for supervised 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.LG(Machine Learning)