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: "transform invariant convolutional neural networks for image classification and search"
  Abstract: Convolutional neural networks (CNNs) have achieved state-of-the-art results on many visual recognition tasks. However, current CNN models still exhibit a poor ability to be invariant to spatial transformations of images. Intuitively, with sufficient layers and parameters, hierarchical combinations of convolution (matrix multiplication and non-linear activation) and pooling operations should be able to learn a robust mapping from transformed input images to transform-invariant representations. In this paper, we propose randomly transforming (rotation, scale, and translation) feature maps of CNNs during the training stage. This prevents complex dependencies of specific rotation, scale, and translation levels of training images in CNN models. Rather, each convolutional kernel learns to detect a feature that is generally helpful for producing the transform-invariant answer given the combinatorially large variety of transform levels of its input feature maps. In this way, we do not require any extra training supervision or modification to the optimization process and training images. We show that random transformation provides significant improvements of CNNs on many benchmark tasks, including small-scale image recognition, large-scale image recognition, and image retrieval. The code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "towards a better match in siamese network based visual object tracker"):
  with members:
    - "a twofold siamese network for real time object tracking"
    - "recurrent filter learning for visual tracking"
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "oriented response networks"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "learning background aware correlation filters for visual tracking"
    - "eco efficient convolution operators for tracking"

Connected through hyperedge HE-2 (induced by source paper "geometric robustness of deep networks analysis and improvement"):
  with members:
    - "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"
    - "adversarial manipulation of deep representations"

Connected through hyperedge HE-3 (induced by source paper "transform invariant auto encoder"):
  with members:
    - "spatial transformer networks"
    - "k sparse autoencoders"

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)