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 "learning pixel wise labeling from the internet without human interaction"):
  Abstract: Deep learning stands at the forefront in many computer vision tasks. However, deep neural networks are usually data-hungry and require a huge amount of well-annotated training samples. Collecting sufficient annotated data is very expensive in many applications, especially for pixel-level prediction tasks such as semantic segmentation. To solve this fundamental issue, we consider a new challenging vision task, Internetly supervised semantic segmentation, which only uses Internet data with noisy image-level supervision of corresponding query keywords for segmentation model training. We address this task by proposing the following solution. A class-specific attention model unifying multiscale forward and backward convolutional features is proposed to provide initial segmentation "ground truth". The model trained with such noisy annotations is then improved by an online fine-tuning procedure. It achieves state-of-the-art performance under the weakly-supervised setting on PASCAL VOC2012 dataset. The proposed framework also paves a new way towards learning from the Internet without human interaction and could serve as a strong baseline therein. Code and data will be released upon the paper acceptance.

Includes members:
  Member "deeply supervised salient object detection with short connections":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "learning random walk label propagation for weakly supervised semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weakly supervised salient object detection via scribble annotations")
  Member "object region mining with adversarial erasing a simple classification to semantic segmentation approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "explainable deep classification models for domain generalization")
  Member "learning to segment every thing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "richer convolutional features for edge detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cafenet class agnostic few shot edge detection network")
  Member "stc a simple to complex framework for weakly supervised semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised semantic segmentation by iterative affinity learning")
  Member "multiscale combinatorial grouping for image segmentation and object proposal generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image")
  Member "seed expand and constrain three principles for weakly supervised image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")

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)