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: "scattering networks for hybrid representation learning"
  Abstract: Scattering networks are a class of designed Convolutional Neural Networks (CNNs) with fixed weights. We argue they can serve as generic representations for modelling images. In particular, by working in scattering space, we achieve competitive results both for supervised and unsupervised learning tasks, while making progress towards constructing more interpretable CNNs. For supervised learning, we demonstrate that the early layers of CNNs do not necessarily need to be learned, and can be replaced with a scattering network instead. Indeed, using hybrid architectures, we achieve the best results with predefined representations to-date, while being competitive with end-to-end learned CNNs. Specifically, even applying a shallow cascade of small-windowed scattering coefficients followed by $1\times 1$1×1-convolutions results in AlexNet accuracy on the ILSVRC2012 classification task. Moreover, by combining scattering networks with deep residual networks, we achieve a single-crop top-5 error of 11.4 percent on ILSVRC2012. Also, we show they can yield excellent performance in the small sample regime on CIFAR-10 and STL-10 datasets, exceeding their end-to-end counterparts, through their ability to incorporate geometrical priors. For unsupervised learning, scattering coefficients can be a competitive representation that permits image recovery. We use this fact to train hybrid GANs to generate images. Finally, we empirically analyze several properties related to stability and reconstruction of images from scattering coefficients.

Connected through hyperedge HE-1 (induced by source paper "harmonic convolutional networks based on discrete cosine transform"):
  with members:
    - "basisconv a method for compressed representation and learning in cnns"
    - "hybrid task cascade for instance segmentation"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "wavelet based edge feature enhancement for convolutional neural networks"
    - "progressive neural architecture search"
    - "random erasing data augmentation"
    - "learning transferable architectures for scalable image recognition"
    - "focal loss for dense object detection"

Connected through hyperedge HE-2 (induced by source paper "biologically inspired architectures for sample efficient deep reinforcement learning"):
  with members:
    - "random expert distillation imitation learning via expert policy support estimation"
    - "weight agnostic neural networks"
    - "when to use parametric models in reinforcement learning"
    - "model based reinforcement learning for atari"
    - "kymatio scattering transforms in python"
    - "generative networks as inverse problems with scattering transforms"
    - "playing atari with six neurons"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "harmonic networks with limited training samples"):
  with members:
    - "gabor convolutional networks"
    - "wavelet based edge feature enhancement for convolutional neural networks"
    - "harmonic networks integrating spectral information into cnns"
    - "efficient convolutional network learning using parametric log based dual tree wavelet scatternet"
    - "a multi branch convolutional neural network for detecting double jpeg compression"
    - "aligned and non aligned double jpeg detection using convolutional neural networks"
    - "wavelet convolutional neural networks for texture classification"
    - "improved regularization of convolutional neural networks with cutout"

Connected through hyperedge HE-4 (induced by source paper "deep network classification by scattering and homotopy dictionary learning"):
  with members:
    - "deep dictionary learning a parametric network approach"
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "kymatio scattering transforms in python"
    - "scaling the scattering transform deep hybrid networks"
    - "supervised deep sparse coding networks"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"
    - "deep roto translation scattering for object classification"

Connected through hyperedge HE-5 (induced by source paper "greedy layerwise learning can scale to imagenet"):
  with members:
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "improved learning of one hidden layer convolutional neural networks with overlaps"
    - "a provably correct algorithm for deep learning that actually works"
    - "i revnet deep invertible networks"
    - "learning one hidden layer neural networks with landscape design"
    - "layer wise training of deep networks using kernel similarity"

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)