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: "visualizing and improving scattering networks"
  Abstract: Scattering Transforms (or ScatterNets) introduced by Mallat are a promising start into creating a well-defined feature extractor to use for pattern recognition and image classification tasks. They are of particular interest due to their architectural similarity to Convolutional Neural Networks (CNNs), while requiring no parameter learning and still performing very well (particularly in constrained classification tasks). #R##N#In this paper we visualize what the deeper layers of a ScatterNet are sensitive to using a 'DeScatterNet'. We show that the higher orders of ScatterNets are sensitive to complex, edge-like patterns (checker-boards and rippled edges). These complex patterns may be useful for texture classification, but are quite dissimilar from the patterns visualized in second and third layers of Convolutional Neural Networks (CNNs) - the current state of the art Image Classifiers. We propose that this may be the source of the current gaps in performance between ScatterNets and CNNs (83% vs 93% on CIFAR-10 for ScatterNet+SVM vs ResNet). We then use these visualization tools to propose possible enhancements to the ScatterNet design, which show they have the power to extract features more closely resembling CNNs, while still being well-defined and having the invariance properties fundamental to ScatterNets.

Connected through hyperedge HE-1 (induced by source paper "a learnable scatternet locally invariant convolutional layers"):
  with members:
    - "deep learning in the wavelet domain"
    - "scaling the scattering transform deep hybrid networks"
    - "dual tree wavelet scattering network with parametric log transformation for object classification"
    - "dynamic steerable blocks in deep residual networks"
    - "harmonic networks deep translation and rotation equivariance"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "wide residual networks"

Connected through hyperedge HE-2 (induced by source paper "cubenet equivariance to 3d rotation and translation"):
  with members:
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "learning steerable filters for rotation equivariant cnns"
    - "polar transformer networks"
    - "rotation equivariant vector field networks"
    - "oriented response networks"
    - "dynamic routing between capsules"
    - "deep rotation equivariant network"

Connected through hyperedge HE-3 (induced by source paper "deep learning in the wavelet domain"):
  with members:
    - "detailed dense inference with convolutional neural networks via discrete wavelet transform"
    - "scaling the scattering transform deep hybrid networks"
    - "scatternet hybrid deep learning shdl network for object classification"
    - "wavelet convolutional neural networks for texture classification"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-4 (induced by source paper "efficient convolutional network learning using parametric log based dual tree wavelet scatternet"):
  with members:
    - "dual tree wavelet scattering network with parametric log transformation for object classification"
    - "wide residual networks"
    - "multi resolution dual tree wavelet scattering network for signal classification"
    - "learning from synthetic data using a stacked multichannel autoencoder"
    - "very deep convolutional networks for large scale image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "deep roto translation scattering for object classification"
    - "deep epitomic convolutional neural networks"

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.CV(Computer Vision and Pattern Recognition)