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: "visual representations defining properties and deep approximations"
  Abstract: Visual representations are defined in terms of minimal sufficient statistics of visual data, for a class of tasks, that are also invariant to nuisance variability. Minimal sufficiency guarantees that we can store a representation in lieu of raw data with smallest complexity and no performance loss on the task at hand. Invariance guarantees that the statistic is constant with respect to uninformative transformations of the data. We derive analytical expressions for such representations and show they are related to feature descriptors commonly used in computer vision, as well as to convolutional neural networks. This link highlights the assumptions and approximations tacitly assumed by these methods and explains empirical practices such as clamping, pooling and joint normalization.

Connected through hyperedge HE-1 (induced by source paper "a cyclically trained adversarial network for invariant representation learning"):
  with members:
    - "a two step disentanglement method"
    - "domain generalization via conditional invariant representation"
    - "disentangling factors of variation with cycle consistent variational auto encoders"
    - "vgan based image representation learning for privacy preserving facial expression recognition"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "towards the automatic anime characters creation with generative adversarial networks"
    - "controllable invariance through adversarial feature learning"
    - "fader networks manipulating images by sliding attributes"

Connected through hyperedge HE-2 (induced by source paper "translation insensitive cnns"):
  with members:
    - "spatially adaptive filter units for compact and efficient deep neural networks"
    - "blurring the line between structure and learning to optimize and adapt receptive fields"
    - "making convolutional networks shift invariant again"
    - "a systematic framework for natural perturbations from videos"
    - "rotdcf decomposition of convolutional filters for rotation equivariant deep networks"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "emergence of invariance and disentanglement in deep representations"
    - "structured receptive fields in cnns"

Connected through hyperedge HE-3 (induced by source paper "dense labeling of large remote sensing imagery with convolutional neural networks a simple and faster alternative to stitching output label maps"):
  with members:
    - "application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "semantic labeling in very high resolution images via a self cascaded convolutional neural network"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "satellite imagery feature detection using deep convolutional neural network a kaggle competition"
    - "rethinking atrous convolution for semantic image segmentation"
    - "automatic detection of solar photovoltaic arrays in high resolution aerial imagery"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"

Connected through hyperedge HE-4 (induced by source paper "tiling and stitching segmentation output for remote sensing basic challenges and recommendations"):
  with members:
    - "application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "satellite imagery feature detection using deep convolutional neural network a kaggle competition"
    - "rethinking atrous convolution for semantic image segmentation"
    - "automatic detection of solar photovoltaic arrays in high resolution aerial imagery"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-5 (induced by source paper "deep learning for passive synthetic aperture radar"):
  with members:
    - "deep convolutional neural network for inverse problems in imaging"
    - "recurrent inference machines for solving inverse problems"
    - "learning to learn by gradient descent by gradient descent"
    - "onsager corrected deep learning for sparse linear inverse problems"
    - "a deep learning approach to block based compressed sensing of images"
    - "a deep learning approach to structured signal recovery"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"
    - "phase retrieval via wirtinger flow theory and algorithms"

Connected through hyperedge HE-6 (induced by source paper "invariant representations without adversarial training"):
  with members:
    - "learning controllable fair representations"
    - "emergence of invariance and disentanglement in deep representations"
    - "controllable invariance through adversarial feature learning"
    - "on the emergence of invariance and disentangling in deep representations"
    - "fader networks manipulating images by sliding attributes"
    - "anchored correlation explanation topic modeling with minimal domain knowledge"
    - "deep variational information bottleneck"
    - "group equivariant convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "emergence of invariance and disentanglement in deep representations"):
  with members:
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "sharp minima can generalize for deep nets"
    - "opening the black box of deep neural networks via information"
    - "on the ability of neural nets to express distributions"
    - "deep variational information bottleneck"
    - "understanding deep learning requires rethinking generalization"
    - "entropy sgd biasing gradient descent into wide valleys"

Connected through hyperedge HE-8 (induced by source paper "geometric robustness of deep networks analysis and improvement"):
  with members:
    - "transform invariant convolutional neural networks for image classification and search"
    - "robust physical world attacks on deep learning models"
    - "deformable convolutional networks"
    - "adversarial transformation networks learning to generate adversarial examples"
    - "towards evaluating the robustness of neural networks"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "manitest are classifiers really invariant"

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)