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: "studying invariances of trained convolutional neural networks"
  Abstract: Convolutional Neural Networks (CNNs) define an exceptionally powerful class of models for image classification, but the theoretical background and the understanding of how invariances to certain transformations are learned is limited. In a large scale screening with images modified by different affine and nonaffine transformations of varying magnitude, we analyzed the behavior of the CNN architectures AlexNet and ResNet. If the magnitude of different transformations does not exceed a class- and transformation dependent threshold, both architectures show invariant behavior. In this work we furthermore introduce a new learnable module, the Invariant Transformer Net, which enables us to learn differentiable parameters for a set of affine transformations. This allows us to extract the space of transformations to which the CNN is invariant and its class prediction robust.

Connected through hyperedge HE-1 (induced by source paper "why do deep convolutional networks generalize so poorly to small image transformations"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "making convolutional networks shift invariant again"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "exploring the space of black box attacks on deep neural networks"
    - "the elephant in the room"
    - "quantifying translation invariance in convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "the effect of learning strategy versus inherent architecture properties on the ability of convolutional neural networks to develop transformation invariance"):
  with members:
    - "patch reordering a novel way to achieve rotation and translation invariance in convolutional neural networks"
    - "quantifying translation invariance in convolutional neural networks"
    - "dynamic routing between capsules"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "group equivariant convolutional networks"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

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)