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 "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"):
  Abstract: This work provides a simple approach to discover tight object bounding boxes with only image-level supervision, called Tight box mining with Surrounding Segmentation Context (TS2C). We observe that object candidates mined through current multiple instance learning methods are usually trapped to discriminative object parts, rather than the entire object. TS2C leverages surrounding segmentation context derived from weakly-supervised segmentation to suppress such low-quality distracting candidates and boost the high-quality ones. Specifically, TS2C is developed based on two key properties of desirable bounding boxes: 1) high purity, meaning most pixels in the box are with high object response, and 2) high completeness, meaning the box covers high object response pixels comprehensively. With such novel and computable criteria, more tight candidates can be discovered for learning a better object detector. With TS2C, we obtain 48.0% and 44.4% mAP scores on VOC 2007 and 2012 benchmarks, which are the new state-of-the-arts.

Includes members:
  Member "self produced guidance for weakly supervised object localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "starnet towards weakly supervised few shot detection and explainable few shot classification")
  Member "adversarial complementary learning for weakly supervised object localization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "explainable deep classification models for domain generalization")
  Member "revisiting rcnn on awakening the classification power of faster rcnn":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "revisiting the sibling head in object detector")
  Member "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised localization")
  Member "deep self taught learning for weakly supervised object localization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ehsod cam guided end to end hybrid supervised object detection with cascade refinement")
  Member "object region mining with adversarial erasing a simple classification to semantic segmentation approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "explainable deep classification models for domain generalization")
  Member "stc a simple to complex framework for weakly supervised semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weakly supervised semantic segmentation by iterative affinity learning")
  Member "dsod learning deeply supervised object detectors from scratch":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "joint anchor feature refinement for real time accurate object detection in images and videos")

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)