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: "transfer learning by ranking for weakly supervised object annotation"
  Abstract: Most existing approaches to training object detectors rely on fully supervised learning, which requires the tedious manual annotation of object location in a training set. Recently there has been an increasing interest in developing weakly supervised approach to detector training where the object location is not manually annotated but automatically determined based on binary (weak) labels indicating if a training image contains the object. This is a challenging problem because each image can contain many candidate object locations which partially overlaps the object of interest. Existing approaches focus on how to best utilise the binary labels for object location annotation. In this paper we propose to solve this problem from a very different perspective by casting it as a transfer learning problem. Specifically, we formulate a novel transfer learning based on learning to rank, which effectively transfers a model for automatic annotation of object location from an auxiliary dataset to a target dataset with completely unrelated object categories. We show that our approach outperforms existing state-of-the-art weakly supervised approach to annotating objects in the challenging VOC dataset.

Connected through hyperedge HE-1 (induced by source paper "m adda unsupervised domain adaptation with deep metric learning"):
  with members:
    - "deep visual domain adaptation a survey"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "unsupervised domain adaptation with similarity learning"
    - "adversarial discriminative domain adaptation"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "self ensembling for visual domain adaptation"
    - "from source to target and back symmetric bi directional adaptive gan"
    - "learning to cluster in order to transfer across domains and tasks"

Connected through hyperedge HE-2 (induced by source paper "training object detectors from few weakly labeled and many unlabeled images"):
  with members:
    - "weakly supervised object detection with segmentation collaboration"
    - "object aware instance labeling for weakly supervised object detection"
    - "few shot object detection with attention rpn and multi relation detector"
    - "few example object detection with model communication"
    - "feature weighting and boosting for few shot segmentation"
    - "label propagation for deep semi supervised learning"
    - "adaptive masked proxies for few shot segmentation"
    - "mixmatch a holistic approach to semi supervised learning"

Connected through hyperedge HE-3 (induced by source paper "large scale weakly supervised pre training for video action recognition"):
  with members:
    - "scsampler sampling salient clips from video for efficient action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "exploring the limits of weakly supervised pretraining"
    - "videos as space time region graphs"
    - "object level visual reasoning in videos"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised gaussian networks for action detection"):
  with members:
    - "w talc weakly supervised temporal activity localization and classification"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "action sets weakly supervised action segmentation without ordering constraints"
    - "what actions are needed for understanding human actions in videos"
    - "discriminatively learned hierarchical rank pooling networks"

Connected through hyperedge HE-5 (induced by source paper "w talc weakly supervised temporal activity localization and classification"):
  with members:
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "temporal action detection with structured segment networks"
    - "deep self taught learning for weakly supervised object localization"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "weakly supervised action localization by sparse temporal pooling network"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "sampling techniques for large scale object detection from sparsely annotated objects"):
  with members:
    - "part aware fine grained object categorization using weakly supervised part detection network"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "soft sampling for robust object detection"
    - "solution for large scale hierarchical object detection datasets with incomplete annotation and data imbalance"
    - "couplenet coupling global structure with local parts for object detection"
    - "focal loss for dense object detection"
    - "squeeze and excitation networks"
    - "chainercv a library for deep learning in computer vision"

Connected through hyperedge HE-7 (induced by source paper "mixed supervised object detection with robust objectness transfer"):
  with members:
    - "weakly supervised object localization using things and stuff transfer"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "improving weakly supervised object localization by micro annotation"
    - "training region based object detectors with online hard example mining"
    - "weakly supervised cascaded convolutional networks"
    - "contextlocnet context aware deep network models for weakly supervised localization"
    - "r fcn object detection via region based fully convolutional networks"
    - "weakly supervised deep detection networks"

Connected through hyperedge HE-8 (induced by source paper "dock detecting objects by transferring common sense knowledge"):
  with members:
    - "visir visual and semantic image label refinement"
    - "phrase localization and visual relationship detection with comprehensive linguistic cues"
    - "the more you know using knowledge graphs for image classification"
    - "phrase localization and visual relationship detection with comprehensive image language cues"
    - "visual relationship detection with language priors"
    - "ask me anything free form visual question answering based on knowledge from external sources"
    - "weakly supervised deep detection networks"
    - "fast r cnn"

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)