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: "understanding image representations by measuring their equivariance and equivalence"
  Abstract: Despite the importance of image representations such as histograms of oriented gradients and deep Convolutional Neural Networks (CNN), our theoretical understanding of them remains limited. Aiming at filling this gap, we investigate three key mathematical properties of representations: equivariance, invariance, and equivalence. Equivariance studies how transformations of the input image are encoded by the representation, invariance being a special case where a transformation has no effect. Equivalence studies whether two representations, for example two different parametrisations of a CNN, capture the same visual information or not. A number of methods to establish these properties empirically are proposed, including introducing transformation and stitching layers in CNNs. These methods are then applied to popular representations to reveal insightful aspects of their structure, including clarifying at which layers in a CNN certain geometric invariances are achieved. While the focus of the paper is theoretical, direct applications to structured-output regression are demonstrated too.

Connected through hyperedge HE-1 (induced by source paper "incorporating symmetry into deep dynamics models for improved generalization"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "general e 2 equivariant steerable cnns"
    - "invariant representations through adversarial forgetting"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "equivariant neural networks and equivarification"
    - "invariant representations without adversarial training"
    - "neural ordinary differential equations"
    - "learning steerable filters for rotation equivariant cnns"

Connected through hyperedge HE-2 (induced by source paper "efficient training of deep convolutional neural networks by augmentation in embedding space"):
  with members:
    - "jointdnn an efficient training and inference engine for intelligent mobile cloud computing services"
    - "a meta learning approach for custom model training"
    - "an analysis of deep neural network models for practical applications"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "a bayesian data augmentation approach for learning deep models"
    - "group equivariant convolutional networks"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "on the fairness of disentangled representations"):
  with members:
    - "are disentangled representations helpful for abstract visual reasoning"
    - "learning disentangled representations with reference based variational autoencoders"
    - "on variational bounds of mutual information"
    - "competitive training of mixtures of independent deep generative models"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "visual reinforcement learning with imagined goals"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-4 (induced by source paper "making convolutional networks shift invariant again"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "using pre training can improve model robustness and uncertainty"
    - "spatially transformed adversarial examples"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a style based generator architecture for generative adversarial networks"
    - "pooling is neither necessary nor sufficient for appropriate deformation stability in cnns"
    - "why do deep convolutional networks generalize so poorly to small image transformations"

Connected through hyperedge HE-5 (induced by source paper "graphter unsupervised learning of graph transformation equivariant representations via auto encoding node wise transformations"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "avt unsupervised learning of transformation equivariant representations by autoencoding variational transformations"
    - "learning generalized transformation equivariant representations via autoencoding transformations"
    - "l2g auto encoder understanding point clouds by local to global reconstruction with hierarchical self attention"
    - "relation shape convolutional neural network for point cloud analysis"
    - "multi angle point cloud vae unsupervised feature learning for 3d point clouds from multiple angles by joint self reconstruction and half to half prediction"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"

Connected through hyperedge HE-6 (induced by source paper "disentangling factors of variation using few labels"):
  with members:
    - "are disentangled representations helpful for abstract visual reasoning"
    - "learning disentangled representations with reference based variational autoencoders"
    - "on the fairness of disentangled representations"
    - "competitive training of mixtures of independent deep generative models"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "visual reinforcement learning with imagined goals"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-7 (induced by source paper "g softmax improving intra class compactness and inter class separability of features"):
  with members:
    - "snapshot ensembles train 1 get m for free"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "order free rnn with visual attention for multi label classification"
    - "miml fcn multi instance multi label learning via fully convolutional networks with privileged information"
    - "dilated residual networks"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "improving pairwise ranking for multi label image classification"
    - "densely connected convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "domainsiam domain aware siamese network for visual object tracking"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "target aware deep tracking"
    - "atom accurate tracking by overlap maximization"
    - "denssiam end to end densely siamese network with self attention model for object tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "a twofold siamese network for real time object tracking"
    - "lasot a high quality benchmark for large scale single object tracking"

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)