human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "cap2det learning to amplify weak caption supervision for object detection"):
  Abstract: Learning to localize and name object instances is a fundamental problem in vision, but state-of-the-art approaches rely on expensive bounding box supervision. While weakly supervised detection (WSOD) methods relax the need for boxes to that of image-level annotations, even cheaper supervision is naturally available in the form of unstructured textual descriptions that users may freely provide when uploading image content. However, straightforward approaches to using such data for WSOD wastefully discard captions that do not exactly match object names. Instead, we show how to squeeze the most information out of these captions by training a text-only classifier that generalizes beyond dataset boundaries. Our discovery provides an opportunity for learning detection models from noisy but more abundant and freely-available caption data. We also validate our model on three classic object detection benchmarks and achieve state-of-the-art WSOD performance. Our code is available at this https URL.

Includes members:
  Member "min entropy latent model for weakly supervised object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weakly supervised instance segmentation by deep community learning")
  Member "equal but not the same understanding the implicit relationship between persuasive images and text":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep multi modal sets")
  Member "neural baby talk":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised instance segmentation by deep community learning")
  Member "deep learning for generic object detection a survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "domain adaptive faster r cnn for object detection in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")
  Member "pcl proposal cluster learning for weakly supervised object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "starnet towards weakly supervised few shot detection and explainable few shot classification")
  Member "artistic object recognition by unsupervised style adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "restyling data application to unsupervised domain adaptation")

Question: Please tell me which class the source paper 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)