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: "learning stable group invariant representations with convolutional networks"
  Abstract: Transformation groups, such as translations or rotations, effectively express part of the variability observed in many recognition problems. The group structure enables the construction of invariant signal representations with appealing mathematical properties, where convolutions, together with pooling operators, bring stability to additive and geometric perturbations of the input. Whereas physical transformation groups are ubiquitous in image and audio applications, they do not account for all the variability of complex signal classes. #R##N#We show that the invariance properties built by deep convolutional networks can be cast as a form of stable group invariance. The network wiring architecture determines the invariance group, while the trainable filter coefficients characterize the group action. We give explanatory examples which illustrate how the network architecture controls the resulting invariance group. We also explore the principle by which additional convolutional layers induce a group factorization enabling more abstract, powerful invariant representations.

Connected through hyperedge HE-1 (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"
    - "scaling the scattering transform deep hybrid networks"
    - "look listen and learn"
    - "improved training of wasserstein gans"
    - "inverse problems with invariant multiscale statistics"
    - "what makes imagenet good for transfer learning"

Connected through hyperedge HE-2 (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-3 (induced by source paper "scaling the scattering transform deep hybrid networks"):
  with members:
    - "deep unsupervised learning through spatial contrasting"
    - "building a regular decision boundary with deep networks"
    - "multiscale hierarchical convolutional networks"
    - "inverse problems with invariant multiscale statistics"
    - "what makes imagenet good for transfer learning"
    - "wide residual networks"
    - "learning both weights and connections for efficient neural networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "on the effect of pooling on the geometry of representations"):
  with members:
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "local group invariant representations via orbit embeddings"
    - "group equivariant convolutional networks"
    - "learning with group invariant features a kernel perspective"
    - "understanding deep features with computer generated imagery"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"
    - "on invariance and selectivity in representation learning"
    - "understanding image representations by measuring their equivariance and equivalence"

Connected through hyperedge HE-5 (induced by source paper "emergence of selective invariance in hierarchical feed forward networks"):
  with members:
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "group equivariant convolutional networks"
    - "on invariance and selectivity in representation learning"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "fractional max pooling"
    - "stochastic pooling for regularization of deep convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "how convnets model non linear transformations"):
  with members:
    - "global optimality in tensor factorization deep learning and beyond"
    - "on the expressive power of deep learning a tensor analysis"
    - "on the expressive efficiency of sum product networks"
    - "why does deep learning work a perspective from group theory"
    - "unsupervised learning of invariant representations in hierarchical architectures"

Connected through hyperedge HE-7 (induced by source paper "deep neural networks with random gaussian weights a universal classification strategy"):
  with members:
    - "deep learning in neural networks"
    - "geometry aware deep transform"
    - "understanding deep image representations by inverting them"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "the loss surfaces of multilayer networks"
    - "convolutional kernel networks"

Connected through hyperedge HE-8 (induced by source paper "warped convolutions efficient invariance to spatial transformations"):
  with members:
    - "learning rotation invariant convolutional filters for texture classification"
    - "group equivariant convolutional networks"
    - "spatial transformer networks"
    - "fast algorithms for convolutional neural networks"
    - "adam a method for stochastic optimization"
    - "locally scale invariant convolutional neural networks"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "matconvnet convolutional neural networks for matlab"

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.AI(Artificial Intelligence)