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 "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"):
  Abstract: The main obstacle to weakly supervised semantic image segmentation is the difficulty of obtaining pixel-level information from coarse image-level annotations. Most methods based on image-level annotations use localization maps obtained from the classifier, but these only focus on the small discriminative parts of objects and do not capture precise boundaries. FickleNet explores diverse combinations of locations on feature maps created by generic deep neural networks. It selects hidden units randomly and then uses them to obtain activation scores for image classification. FickleNet implicitly learns the coherence of each location in the feature maps, resulting in a localization map which identifies both discriminative and other parts of objects. The ensemble effects are obtained from a single network by selecting random hidden unit pairs, which means that a variety of localization maps are generated from a single image. Our approach does not require any additional training steps and only adds a simple layer to a standard convolutional neural network; nevertheless it outperforms recent comparable techniques on the Pascal VOC 2012 benchmark in both weakly and semi-supervised settings.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  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-3 (induced by source paper "weakly supervised instance segmentation by deep community learning")
  Member "weakly supervised semantic segmentation by iteratively mining common object features":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (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-6 (induced by source paper "frame to frame aggregation of active regions in web videos for weakly supervised semantic segmentation")
  Member "on regularized losses for weakly supervised cnn segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weakly supervised salient object detection via scribble annotations")
  Member "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised 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)