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: "on learning where to look"
  Abstract: Current automatic vision systems face two major challenges: scalability and extreme variability of appearance. First, the computational time required to process an image typically scales linearly with the number of pixels in the image, therefore limiting the resolution of input images to thumbnail size. Second, variability in appearance and pose of the objects constitute a major hurdle for robust recognition and detection. In this work, we propose a model that makes baby steps towards addressing these challenges. We describe a learning based method that recognizes objects through a series of glimpses. This system performs an amount of computation that scales with the complexity of the input rather than its number of pixels. Moreover, the proposed method is potentially more robust to changes in appearance since its parameters are learned in a data driven manner. Preliminary experiments on a handwritten dataset of digits demonstrate the computational advantages of this approach.

Connected through hyperedge HE-1 (induced by source paper "pay attention to the activations a modular attention mechanism for fine grained image recognition"):
  with members:
    - "object part attention model for fine grained image classification"
    - "multi attention multi class constraint for fine grained image recognition"
    - "learning to navigate for fine grained classification"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "attend and rectify a gated attention mechanism for fine grained recovery"
    - "learn to pay attention"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "pack and detect fast object detection in videos using region of interest packing"):
  with members:
    - "yolov3 an incremental improvement"
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "recurrent residual module for fast inference in videos"
    - "object detection in videos by short and long range object linking"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "fast yolo a fast you only look once system for real time embedded object detection in video"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "towards high performance video object detection"

Connected through hyperedge HE-3 (induced by source paper "processing megapixel images with deep attention sampling models"):
  with members:
    - "classification of breast cancer histology using deep learning"
    - "variational saccading efficient inference for large resolution images"
    - "two stage convolutional neural network for breast cancer histology image classification"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "detecting cancer metastases on gigapixel pathology images"
    - "ssd single shot multibox detector"
    - "wider or deeper revisiting the resnet model for visual recognition"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-4 (induced by source paper "learning where to see a novel attention model for automated immunohistochemical scoring"):
  with members:
    - "fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features"
    - "her2 challenge contest a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues"
    - "fully convolutional attention networks for fine grained recognition"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "action recognition using visual attention"
    - "active object localization with deep reinforcement learning"
    - "going deeper with convolutions"

Connected through hyperedge HE-5 (induced by source paper "variational saccading efficient inference for large resolution images"):
  with members:
    - "a new benchmark and progress toward improved weakly supervised learning"
    - "visual interpretability for deep learning a survey"
    - "group normalization"
    - "towards interpretable r cnn by unfolding latent structures"
    - "hierarchical attentive recurrent tracking"
    - "a simple neural network module for relational reasoning"
    - "starcraft ii a new challenge for reinforcement learning"
    - "dynamic routing between capsules"

Connected through hyperedge HE-6 (induced by source paper "geometry aware recurrent neural networks for active visual recognition"):
  with members:
    - "3d interpreter networks for viewer centered wireframe modeling"
    - "cycada cycle consistent adversarial domain adaptation"
    - "learning a multi view stereo machine"
    - "a dataset for developing and benchmarking active vision"
    - "unsupervised learning of depth and ego motion from video"
    - "neural map structured memory for deep reinforcement learning"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "marrnet 3d shape reconstruction via 2 5d sketches"

Connected through hyperedge HE-7 (induced by source paper "reinforcement learning of active vision for manipulating objects under occlusions"):
  with members:
    - "learning 6 dof grasping and pick place using attention focus"
    - "self supervised visual planning with temporal skip connections"
    - "hindsight experience replay"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "a dataset for developing and benchmarking active vision"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "neural task programming learning to generalize across hierarchical tasks"
    - "asymmetric actor critic for image based robot learning"

Connected through hyperedge HE-8 (induced by source paper "deep reinforcement learning for visual object tracking in videos"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"
    - "fully convolutional siamese networks for object tracking"
    - "spatially supervised recurrent convolutional neural networks for visual object tracking"
    - "learning to track at 100 fps with deep regression networks"
    - "deeptrack learning discriminative feature representations online for robust visual tracking"
    - "transferring rich feature hierarchies for robust visual tracking"
    - "unsupervised learning of video representations using lstms"
    - "first step toward model free anonymous object tracking with recurrent neural networks"

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)