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: "scaling the scattering transform deep hybrid networks"
  Abstract: We use the scattering network as a generic and fixed ini-tialization of the first layers of a supervised hybrid deep network. We show that early layers do not necessarily need to be learned, providing the best results to-date with pre-defined representations while being competitive with Deep CNNs. Using a shallow cascade of 1 x 1 convolutions, which encodes scattering coefficients that correspond to spatial windows of very small sizes, permits to obtain AlexNet accuracy on the imagenet ILSVRC2012. We demonstrate that this local encoding explicitly learns invariance w.r.t. rotations. Combining scattering networks with a modern ResNet, we achieve a single-crop top 5 error of 11.4% on imagenet ILSVRC2012, comparable to the Resnet-18 architecture, while utilizing only 10 layers. We also find that hybrid architectures can yield excellent performance in the small sample regime, exceeding their end-to-end counterparts, through their ability to incorporate geometrical priors. We demonstrate this on subsets of the CIFAR-10 dataset and on the STL-10 dataset.

Connected through hyperedge HE-1 (induced by source paper "analyzing the dependency of convnets on spatial information"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "deformable convnets v2 more deformable better results"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dynamic routing between capsules"
    - "deformable convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "a critical analysis of self supervision or what we can learn from a single image"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "singan learning a generative model from a single natural image"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "cross pixel optical flow similarity for self supervised learning"
    - "unsupervised representation learning by predicting image rotations"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-3 (induced by source paper "unsupervised embedding learning via invariant and spreading instance feature"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "mining on manifolds metric learning without labels"
    - "in defense of the triplet loss for person re identification"
    - "dynamic label graph matching for unsupervised video re identification"
    - "alignedreid surpassing human level performance in person re identification"
    - "significance of softmax based features in comparison to distance metric learning based features"
    - "no fuss distance metric learning using proxies"
    - "learning spread out local feature descriptors"

Connected through hyperedge HE-4 (induced by source paper "scattering networks for hybrid representation learning"):
  with members:
    - "generative networks as inverse problems with scattering transforms"
    - "deep unsupervised learning through spatial contrasting"
    - "building a regular decision boundary with deep networks"
    - "look listen and learn"
    - "improved training of wasserstein gans"
    - "inverse problems with invariant multiscale statistics"
    - "what makes imagenet good for transfer learning"
    - "adversarial feature learning"

Connected through hyperedge HE-5 (induced by source paper "avt unsupervised learning of transformation equivariant representations by autoencoding variational transformations"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "learning generalized transformation equivariant representations via autoencoding transformations"
    - "deep clustering for unsupervised learning of visual features"
    - "group equivariant capsule networks"
    - "unsupervised representation learning by predicting image rotations"
    - "intertwiners between induced representations with applications to the theory of equivariant neural networks"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "dynamic routing between capsules"

Connected through hyperedge HE-6 (induced by source paper "transformations are all you need learning generalized transformation equivariant representations via autoencoding transformations"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "deep clustering for unsupervised learning of visual features"
    - "group equivariant capsule networks"
    - "unsupervised representation learning by predicting image rotations"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "dynamic routing between capsules"
    - "representation learning by learning to count"

Connected through hyperedge HE-7 (induced by source paper "learning generalized transformation equivariant representations via autoencoding transformations"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "deep clustering for unsupervised learning of visual features"
    - "group equivariant capsule networks"
    - "unsupervised representation learning by predicting image rotations"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "dynamic routing between capsules"
    - "representation learning by learning to count"
    - "temporal ensembling for semi supervised learning"

Connected through hyperedge HE-8 (induced by source paper "a learnable scatternet locally invariant convolutional layers"):
  with members:
    - "deep learning in the wavelet domain"
    - "dual tree wavelet scattering network with parametric log transformation for object classification"
    - "visualizing and improving scattering networks"
    - "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"

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)