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: "digging deep into the layers of cnns in search of how cnns achieve view invariance"
  Abstract: This paper is focused on studying the view-manifold structure in the feature spaces implied by the different layers of Convolutional Neural Networks (CNN). There are several questions that this paper aims to answer: Does the learned CNN representation achieve viewpoint invariance? How does it achieve viewpoint invariance? Is it achieved by collapsing the view manifolds, or separating them while preserving them? At which layer is view invariance achieved? How can the structure of the view manifold at each layer of a deep convolutional neural network be quantified experimentally? How does fine-tuning of a pre-trained CNN on a multi-view dataset affect the representation at each layer of the network? In order to answer these questions we propose a methodology to quantify the deformation and degeneracy of view manifolds in CNN layers. We apply this methodology and report interesting results in this paper that answer the aforementioned questions.

Connected through hyperedge HE-1 (induced by source paper "class subset selection for transfer learning using submodularity"):
  with members:
    - "what makes imagenet good for transfer learning"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "context encoders feature learning by inpainting"
    - "discovering internal representations from object cnns using population encoding"
    - "unsupervised visual representation learning by context prediction"
    - "inverting visual representations with convolutional networks"
    - "unsupervised learning of visual representations using videos"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "depthcut improved depth edge estimation using multiple unreliable channels"):
  with members:
    - "semantic scene completion from a single depth image"
    - "scenenet rgb d 5m photorealistic images of synthetic indoor trajectories with ground truth"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "is the deconvolution layer the same as a convolutional layer"
    - "depth from a single image by harmonizing overcomplete local network predictions"
    - "image to image translation with conditional adversarial networks"
    - "data driven shape analysis and processing"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (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"

Connected through hyperedge HE-4 (induced by source paper "3d pose regression using convolutional neural networks"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "6 dof object pose from semantic keypoints"
    - "single image 3d interpreter network"
    - "modelling uncertainty in deep learning for camera relocalization"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "what makes imagenet good for transfer learning"):
  with members:
    - "learning representations for automatic colorization"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "learning without forgetting"
    - "context encoders feature learning by inpainting"
    - "colorful image colorization"
    - "unsupervised visual representation learning by context prediction"
    - "instance aware semantic segmentation via multi task network cascades"
    - "inverting visual representations with convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "what can we learn about cnns from a large scale controlled object dataset"):
  with members:
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "from facial parts responses to face detection a deep learning approach"
    - "convolutional models for joint object categorization and pose estimation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "exploring invariances in deep convolutional neural networks using synthetic images"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "convolutional models for joint object categorization and pose estimation"):
  with members:
    - "3d object class detection in the wild"
    - "factorization of view object manifolds for joint object recognition and pose estimation"
    - "what is holding back convnets for detection"
    - "return of the devil in the details delving deep into convolutional nets"
    - "viewpoints and keypoints"
    - "r cnns for pose estimation and action detection"
    - "imagenet large scale visual recognition challenge"
    - "heterogeneous multi task learning for human pose estimation with deep convolutional neural network"

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)