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: "window object relationship guided representation learning for generic object detections"
  Abstract: In existing works that learn representation for object detection, the relationship between a candidate window and the ground truth bounding box of an object is simplified by thresholding their overlap. This paper shows information loss in this simplification and picks up the relative location/size information discarded by thresholding. We propose a representation learning pipeline to use the relationship as supervision for improving the learned representation in object detection. Such relationship is not limited to object of the target category, but also includes surrounding objects of other categories. We show that image regions with multiple contexts and multiple rotations are effective in capturing such relationship during the representation learning process and in handling the semantic and visual variation caused by different window-object configurations. Experimental results show that the representation learned by our approach can improve the object detection accuracy by 6.4% in mean average precision (mAP) on ILSVRC2014. On the challenging ILSVRC2014 test dataset, 48.6% mAP is achieved by our single model and it is the best among published results. On PASCAL VOC, it outperforms the state-of-the-art result of Fast RCNN by 3.3% in absolute mAP.

Connected through hyperedge HE-1 (induced by source paper "t cnn tubelets with convolutional neural networks for object detection from videos"):
  with members:
    - "cost effective active learning for deep image classification"
    - "object detection in videos with tubelet proposal networks"
    - "object detection from video tubelets with convolutional neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "ssd single shot multibox detector"
    - "seq nms for video object detection"
    - "craft objects from images"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-2 (induced by source paper "crafting gbd net for object detection"):
  with members:
    - "zoom out and in network with recursive training for object proposal"
    - "factors in finetuning deep model for object detection"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "ssd single shot multibox detector"
    - "structured feature learning for pose estimation"
    - "what makes for effective detection proposals"
    - "polynet a pursuit of structural diversity in very deep networks"

Connected through hyperedge HE-3 (induced by source paper "attentive contexts for object detection"):
  with members:
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "hcp a flexible cnn framework for multi label image classification"
    - "ssd single shot multibox detector"
    - "hypernet towards accurate region proposal generation and joint object detection"
    - "you only look once unified real time object detection"
    - "proposal free network for instance level object segmentation"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "show attend and tell neural image caption generation with visual attention"

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)