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 "frame to frame aggregation of active regions in web videos for weakly supervised semantic segmentation"):
  Abstract: When a deep neural network is trained on data with only image-level labeling, the regions activated in each image tend to identify only a small region of the target object. We propose a method of using videos automatically harvested from the web to identify a larger region of the target object by using temporal information, which is not present in the static image. The temporal variations in a video allow different regions of the target object to be activated. We obtain an activated region in each frame of a video, and then aggregate the regions from successive frames into a single image, using a warping technique based on optical flow. The resulting localization maps cover more of the target object, and can then be used as proxy ground-truth to train a segmentation network. This simple approach outperforms existing methods under the same level of supervision, and even approaches relying on extra annotations. Based on VGG-16 and ResNet 101 backbones, our method achieves the mIoU of 65.0 and 67.4, respectively, on PASCAL VOC 2012 test images, which represents a new state-of-the-art.

Includes members:
  Member "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weakly supervised instance segmentation by deep community learning")
  Member "deeply supervised salient object detection with short connections":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "learning a discriminative feature network for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation":
    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 "normalized cut loss for weakly supervised cnn segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "weakly supervised salient object detection via scribble annotations")
  Member "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised instance segmentation by deep community learning")
  Member "adversarial complementary learning for weakly supervised object localization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "explainable deep classification models for domain generalization")
  Member "robust tumor localization with pyramid grad cam":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference")

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)