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: "recovering hard to find object instances by sampling context based object proposals"
  Abstract: Sampling context-based object proposals is effective for recovering missed detections.Topic models can effectively model higher-order relations between objects instances.Context-based proposals are effective for spotting regions that contain objects.Object proposal generation should not be employed solely as a pre-detection step. In this paper we focus on improving object detection performance in terms of recall. We propose a post-detection stage during which we explore the image with the objective of recovering missed detections. This exploration is performed by sampling object proposals in the image. We analyse four different strategies to perform this sampling, giving special attention to strategies that exploit spatial relations between objects. In addition, we propose a novel method to discover higher-order relations between groups of objects. Experiments on the challenging KITTI dataset show that our proposed relations-based proposal generation strategies can help improving recall at the cost of a relatively low amount of object proposals.

Connected through hyperedge HE-1 (induced by source paper "gaussian processes with context supported priors for active object localization"):
  with members:
    - "relief r cnn utilizing convolutional features for fast object detection"
    - "active object localization in visual situations"
    - "how far are we from solving pedestrian detection"
    - "deep feature based contextual model for object detection"
    - "fast on line kernel density estimation for active object localization"
    - "searching for objects using structure in indoor scenes"
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
    - "uavs using bayesian optimization to locate wifi devices"

Connected through hyperedge HE-2 (induced by source paper "contextual object detection with a few relevant neighbors"):
  with members:
    - "inner scene similarities as a contextual cue for object detection"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "the role of context selection in object detection"
    - "attentive contexts for object detection"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-3 (induced by source paper "context based object viewpoint estimation a 2d relational approach"):
  with members:
    - "active object localization in visual situations"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "viewpoints and keypoints"
    - "discriminative probabilistic models for relational data"

Connected through hyperedge HE-4 (induced by source paper "active object localization in visual situations"):
  with members:
    - "deep feature based contextual model for object detection"
    - "visual concept recognition and localization via iterative introspection"
    - "object scene convolutional neural networks for event recognition in images"
    - "searching for objects using structure in indoor scenes"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "segdeepm exploiting segmentation and context in deep neural networks for object detection"
    - "visual semantic role labeling"

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)