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: "training object class detectors with click supervision"
  Abstract: Training object class detectors typically requires a large set of images with objects annotated by bounding boxes. However, manually drawing bounding boxes is very time consuming. In this paper we greatly reduce annotation time by proposing center-click annotations: we ask annotators to click on the center of an imaginary bounding box which tightly encloses the object instance. We then incorporate these clicks into existing Multiple Instance Learning techniques for weakly supervised object localization, to jointly localize object bounding boxes over all training images. Extensive experiments on PASCAL VOC 2007 and MS COCO show that: (1) our scheme delivers high-quality detectors, performing substantially better than those produced by weakly supervised techniques, with a modest extra annotation effort; (2) these detectors in fact perform in a range close to those trained from manually drawn bounding boxes; (3) as the center-click task is very fast, our scheme reduces total annotation time by 9x to 18x.

Connected through hyperedge HE-1 (induced by source paper "towards noise resistant object detection with noisy annotations"):
  with members:
    - "how does disagreement help generalization against label corruption"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "training object detectors with noisy data"
    - "unsupervised label noise modeling and loss correction"
    - "mixmatch a holistic approach to semi supervised learning"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "note rcnn noise tolerant ensemble rcnn for semi supervised object detection"

Connected through hyperedge HE-2 (induced by source paper "learning object scale with click supervision for object detection"):
  with members:
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "collaborative learning for weakly supervised object detection"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "tell me where to look guided attention inference network"
    - "deep self taught learning for weakly supervised object localization"
    - "ssd single shot multibox detector"
    - "weakly supervised cascaded convolutional networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "wslln weakly supervised natural language localization networks"):
  with members:
    - "startnet online detection of action start in untrimmed videos"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "weakly supervised dense event captioning in videos"
    - "temporal recurrent networks for online action detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "to find where you talk temporal sentence localization in video with attention based location regression"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "c wsl count guided weakly supervised localization"

Connected through hyperedge HE-5 (induced by source paper "large scale interactive object segmentation with human annotators"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "weakly supervised instance segmentation using class peak response"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "iteratively trained interactive segmentation"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "revisiting unreasonable effectiveness of data in deep learning era"

Connected through hyperedge HE-6 (induced by source paper "efficient object annotation via speaking and pointing"):
  with members:
    - "natural vocabulary emerges from free form annotations"
    - "scaling egocentric vision the epic kitchens dataset"
    - "where are the blobs counting by localization with point supervision"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "fast object class labelling via speech"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "object referring in visual scene with spoken language"

Connected through hyperedge HE-7 (induced by source paper "an adaptive supervision framework for active learning in object detection"):
  with members:
    - "active learning for deep object detection"
    - "cost effective active learning for deep image classification"
    - "extreme clicking for efficient object annotation"
    - "deep bayesian active learning with image data"
    - "mask r cnn"
    - "soft proposal networks for weakly supervised object localization"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "we don t need no bounding boxes training object class detectors using only human verification"

Connected through hyperedge HE-8 (induced by source paper "deep learning based automatic video annotation tool for self driving car"):
  with members:
    - "one click annotation with guided hierarchical object detection"
    - "seeding deep learning using wireless localization"
    - "lanenet real time lane detection networks for autonomous driving"
    - "focal loss for dense object detection"
    - "simple online and realtime tracking with a deep association metric"
    - "mot16 a benchmark for multi object tracking"
    - "we don t need no bounding boxes training object class detectors using only human verification"
    - "you only look once unified real time object detection"

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)