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: "unsupervised object discovery and localization in the wild part based matching with bottom up region proposals"
  Abstract: This paper addresses unsupervised discovery and localization of dominant objects from a noisy image collection with multiple object classes. The setting of this problem is fully unsupervised, without even image-level annotations or any assumption of a single dominant class. This is far more general than typical colocalization, cosegmentation, or weakly-supervised localization tasks. We tackle the discovery and localization problem using a part-based region matching approach: We use off-the-shelf region proposals to form a set of candidate bounding boxes for objects and object parts. These regions are efficiently matched across images using a probabilistic Hough transform that evaluates the confidence for each candidate correspondence considering both appearance and spatial consistency. Dominant objects are discovered and localized by comparing the scores of candidate regions and selecting those that stand out over other regions containing them. Extensive experimental evaluations on standard benchmarks demonstrate that the proposed approach significantly outperforms the current state of the art in colocalization, and achieves robust object discovery in challenging mixed-class datasets.

Connected through hyperedge HE-1 (induced by source paper "object discovery with a copy pasting gan"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "inserting videos into videos"
    - "monet unsupervised scene decomposition and representation"
    - "unsupervised moving object detection via contextual information separation"
    - "multi object representation learning with iterative variational inference"
    - "learning to generate synthetic data via compositing"
    - "unsupervised image matching and object discovery as optimization"
    - "learning to segment via cut and paste"

Connected through hyperedge HE-2 (induced by source paper "few example object detection with model communication"):
  with members:
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "active self paced learning for cost effective and progressive face identification"
    - "collaborative learning for weakly supervised object detection"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "learning to segment human by watching youtube"

Connected through hyperedge HE-3 (induced by source paper "unsupervised learning of foreground object segmentation"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "unsupervised object segmentation in video by efficient selection of highly probable positive features"
    - "unsupervised learning from video to detect foreground objects in single images"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "unsupervised representation learning by sorting sequences"
    - "segflow joint learning for video object segmentation and optical flow"
    - "unsupervised learning for physical interaction through video prediction"
    - "learning representations for automatic colorization"

Connected through hyperedge HE-4 (induced by source paper "zero shot detection"):
  with members:
    - "probabilistic semantic retrieval for surveillance videos with activity graphs"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "generalized zero shot recognition based on visually semantic embedding"
    - "recent advances in zero shot recognition"
    - "focal loss for dense object detection"
    - "zero shot learning the good the bad and the ugly"
    - "mask r cnn"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-5 (induced by source paper "mining objects fully unsupervised object discovery and localization from a single image"):
  with members:
    - "attention based dropout layer for weakly supervised object localization"
    - "salient object detection in the deep learning era an in depth survey"
    - "salient object detection a survey"
    - "object part attention model for fine grained image classification"
    - "self produced guidance for weakly supervised object localization"
    - "adversarial complementary learning for weakly supervised object localization"
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "diversified visual attention networks for fine grained object classification"

Connected through hyperedge HE-6 (induced by source paper "hyperpixel flow semantic correspondence with multi layer neural features"):
  with members:
    - "semantic matching by weakly supervised 2d point set registration"
    - "exploring randomly wired neural networks for image recognition"
    - "parn pyramidal affine regression networks for dense semantic correspondence"
    - "recurrent transformer networks for semantic correspondence"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "neighbourhood consensus networks"
    - "hierarchical metric learning and matching for 2d and 3d geometric correspondences"
    - "attentive semantic alignment with offset aware correlation kernels"

Connected through hyperedge HE-7 (induced by source paper "harvesting visual objects from internet images via deep learning based objectness assessment"):
  with members:
    - "contrast oriented deep neural networks for salient object detection"
    - "deepqoe a unified framework for learning to predict video qoe"
    - "instance level salient object segmentation"
    - "exploiting web images for dataset construction a domain robust approach"
    - "quasi homography warps in image stitching"
    - "instance sensitive fully convolutional networks"
    - "convolutional oriented boundaries"
    - "salient object subitizing"

Connected through hyperedge HE-8 (induced by source paper "discovering visual patterns in art collections with spatially consistent feature learning"):
  with members:
    - "can computers create art"
    - "neighbourhood consensus networks"
    - "weakly supervised object detection in artworks"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "fine tuning cnn image retrieval with no human annotation"
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "omniart multi task deep learning for artistic data analysis"

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)