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: "multiple instance reinforcement learning for efficient weakly supervised detection in images"
  Abstract: State-of-the-art visual recognition and detection systems increasingly rely on large amounts of training data and complex classifiers. Therefore it becomes increasingly expensive both to manually annotate datasets and to keep running times at levels acceptable for practical applications. In this paper, we propose two solutions to address these issues. First, we introduce a weakly supervised, segmentation-based approach to learn accurate detectors and image classifiers from weak supervisory signals that provide only approximate constraints on target localization. We illustrate our system on the problem of action detection in static images (Pascal VOC Actions 2012), using human visual search patterns as our training signal. Second, inspired from the saccade-and-fixate operating principle of the human visual system, we use reinforcement learning techniques to train efficient search models for detection. Our sequential method is weakly supervised and general (it does not require eye movements), finds optimal search strategies for any given detection confidence function and achieves performance similar to exhaustive sliding window search at a fraction of its computational cost.

Connected through hyperedge HE-1 (induced by source paper "tree structured reinforcement learning for sequential object localization"):
  with members:
    - "what makes for effective detection proposals"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "adaptive object detection using adjacency and zoom prediction"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "active object localization with deep reinforcement learning"
    - "an active search strategy for efficient object class detection"

Connected through hyperedge HE-2 (induced by source paper "gaze embeddings for zero shot image classification"):
  with members:
    - "seeing with humans gaze assisted neural image captioning"
    - "label embedding for image classification"
    - "latent embeddings for zero shot classification"
    - "evaluation of output embeddings for fine grained image classification"
    - "gazedpm early integration of gaze information in deformable part models"
    - "prediction of search targets from fixations in open world settings"
    - "going deeper with convolutions"
    - "the secrets of salient object segmentation"

Connected through hyperedge HE-3 (induced by source paper "predicting the category and attributes of visual search targets using deep gaze pooling"):
  with members:
    - "seeing with humans gaze assisted neural image captioning"
    - "gaze embeddings for zero shot image classification"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "gazedpm early integration of gaze information in deformable part models"
    - "learning deep features for discriminative localization"
    - "prediction of search targets from fixations in open world settings"
    - "the secrets of salient object segmentation"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-4 (induced by source paper "gazedpm early integration of gaze information in deformable part models"):
  with members:
    - "imagenet large scale visual recognition challenge"
    - "pupil an open source platform for pervasive eye tracking and mobile gaze based interaction"
    - "visualizing and understanding convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)