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: "visual and semantic knowledge transfer for large scale semi supervised object detection"
  Abstract: Deep CNN-based object detection systems have achieved remarkable success on several large-scale object detection benchmarks. However, training such detectors requires a large number of labeled bounding boxes, which are more difficult to obtain than image-level annotations. Previous work addresses this issue by transforming image-level classifiers into object detectors. This is done by modeling the differences between the two on categories with both image-level and bounding box annotations, and transferring this information to convert classifiers to detectors for categories without bounding box annotations. We improve this previous work by incorporating knowledge about object similarities from visual and semantic domains during the transfer process. The intuition behind our proposed method is that visually and semantically similar categories should exhibit more common transferable properties than dissimilar categories, e.g. a better detector would result by transforming the differences between a dog classifier and a dog detector onto the cat class, than would by transforming from the violin class. Experimental results on the challenging ILSVRC2013 detection dataset demonstrate that each of our proposed object similarity based knowledge transfer methods outperforms the baseline methods. We found strong evidence that visual similarity and semantic relatedness are complementary for the task, and when combined notably improve detection, achieving state-of-the-art detection performance in a semi-supervised setting.

Connected through hyperedge HE-1 (induced by source paper "cs r fcn cross supervised learning for large scale object detection"):
  with members:
    - "r fcn 3000 at 30fps decoupling detection and classification"
    - "yolo9000 better faster stronger"
    - "r fcn object detection via region based fully convolutional networks"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "lsda large scale detection through adaptation"

Connected through hyperedge HE-2 (induced by source paper "decoupled box proposal and featurization with ultrafine grained semantic labels improve image captioning and visual question answering"):
  with members:
    - "tencent ml images a large scale multi label image database for visual representation learning"
    - "graph rise graph regularized image semantic embedding"
    - "bottom up object detection by grouping extreme and center points"
    - "neural baby talk"
    - "dropblock a regularization method for convolutional networks"
    - "learning to count objects in natural images for visual question answering"
    - "deep learning for generic object detection a survey"
    - "domain adaptive faster r cnn for object detection in the wild"

Connected through hyperedge HE-3 (induced by source paper "scaling object detection by transferring classification weights"):
  with members:
    - "scene graph generation with external knowledge and image reconstruction"
    - "batch normalization is a cause of adversarial vulnerability"
    - "few shot object detection via feature reweighting"
    - "pfdet 2nd place solution to open images challenge 2018 object detection track"
    - "exploring the limits of weakly supervised pretraining"
    - "zero annotation object detection with web knowledge transfer"
    - "revisiting knowledge transfer for training object class detectors"
    - "decoupled weight decay regularization"

Connected through hyperedge HE-4 (induced by source paper "one click annotation with guided hierarchical object detection"):
  with members:
    - "few shot learning with metric agnostic conditional embeddings"
    - "focal loss for dense object detection"
    - "prototypical networks for few shot learning"
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "learning to compare relation network for few shot learning"
    - "annotating object instances with a polygon rnn"
    - "learning intelligent dialogs for bounding box annotation"
    - "weakly and semi supervised object detection with expectation maximization algorithm"

Connected through hyperedge HE-5 (induced by source paper "discriminative and geometry aware unsupervised domain adaptation"):
  with members:
    - "domain adaptation for visual applications a comprehensive survey"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "robust data geometric structure aligned close yet discriminative domain adaptation"
    - "joint geometrical and statistical alignment for visual domain adaptation"
    - "learning transferable features with deep adaptation networks"
    - "deep domain confusion maximizing for domain invariance"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "marginalized denoising autoencoders for domain adaptation"

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)