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 "distill to label weakly supervised instance labeling using knowledge distillation"):
  Abstract: Weakly supervised instance labeling using only image-level labels, in lieu of expensive fine-grained pixel annotations, is crucial in several applications including medical image analysis. In contrast to conventional instance segmentation scenarios in computer vision, the problems that we consider are characterized by a small number of training images and non-local patterns that lead to the diagnosis. In this paper, we explore the use of multiple instance learning (MIL) to design an instance label generator under this weakly supervised setting. Motivated by the observation that an MIL model can handle bags of varying sizes, we propose to repurpose an MIL model originally trained for bag-level classification to produce reliable predictions for single instances, i.e., bags of size $1$. To this end, we introduce a novel regularization strategy based on virtual adversarial training for improving MIL training, and subsequently develop a knowledge distillation technique for repurposing the trained MIL model. Using empirical studies on colon cancer and breast cancer detection from histopathological images, we show that the proposed approach produces high-quality instance-level prediction and significantly outperforms state-of-the MIL methods.

Includes members:
  Member "weakly supervised instance segmentation using class peak response":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weakly supervised instance segmentation by deep community learning")
  Member "attention based deep multiple instance learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "tell me where to look guided attention inference network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "explainable deep classification models for domain generalization")
  Member "invariant information clustering for unsupervised image classification and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a comprehensive approach to unsupervised embedding learning based on and algorithm")
  Member "snorkel rapid training data creation with weak supervision":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "med7 a transferable clinical natural language processing model for electronic health records")
  Member "instance level salient object segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "simple does it weakly supervised instance and semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")

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)