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: "zero annotation object detection with web knowledge transfer"
  Abstract: Object detection is one of the major problems in computer vision, and has been extensively studied. Most of of the existing detection works rely on labor-intensive supervisions, such as ground truth bounding boxes of objects or at least image-level annotations. On the contrary, we propose an object detection method that does not require any form of supervisions on target tasks, by exploiting freely available web images. In order to facilitate effective knowledge transfer from web images, we introduce a multi-instance multi-label domain adaption learning framework with two key innovations. First of all, we propose an instance-level adversarial domain adaptation network with attention on foreground objects to transfer the object appearances from web domain to target domain. Second, to preserve the class-specific semantic structure of transferred object features, we propose a simultaneous transfer mechanism to transfer the supervision across domains through pseudo strong label generation. With our end-to-end framework that simultaneously learns a weakly supervised detector and transfers knowledge across domains, we achieved significant improvements over baseline methods on the benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "sparse and structured visual attention"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "sparse sequence to sequence models"
    - "deep modular co attention networks for visual question answering"
    - "bilinear attention networks"
    - "neural baby talk"
    - "simnet stepwise image topic merging network for generating detailed and comprehensive image captions"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "sparse and constrained attention for neural machine translation"

Connected through hyperedge HE-2 (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"
    - "visual and semantic knowledge transfer for large scale semi supervised object detection"
    - "revisiting knowledge transfer for training object class detectors"
    - "decoupled weight decay regularization"

Connected through hyperedge HE-3 (induced by source paper "detecting 11k classes large scale object detection without fine grained bounding boxes"):
  with members:
    - "yolov3 an incremental improvement"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "sniper efficient multi scale training"
    - "soft sampling for robust object detection"
    - "exploiting web images for weakly supervised object detection"
    - "soft nms improving object detection with one line of code"
    - "extreme clicking for efficient object annotation"
    - "revisiting knowledge transfer for training object class detectors"

Connected through hyperedge HE-4 (induced by source paper "context aware zero shot recognition"):
  with members:
    - "zero shot detection"
    - "classifier and exemplar synthesis for zero shot learning"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "iterative visual reasoning beyond convolutions"
    - "zero shot object detection by hybrid region embedding"
    - "graph r cnn for scene graph generation"
    - "rethinking knowledge graph propagation for zero shot learning"

Connected through hyperedge HE-5 (induced by source paper "shuffle then assemble learning object agnostic visual relationship features"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "context aware visual policy network for sequence level image captioning"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "vip cnn visual phrase guided convolutional neural network"

Connected through hyperedge HE-6 (induced by source paper "exploiting web images for weakly supervised object detection"):
  with members:
    - "how hard can it be estimating the difficulty of visual search in an image"
    - "multiple instance curriculum learning for weakly supervised object detection"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "exploiting web images for dataset construction a domain robust approach"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "multiple instance detection network with online instance classifier refinement"
    - "ssd single shot multibox detector"
    - "weakly supervised cascaded convolutional networks"

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)