human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "where are the masks instance segmentation with image level supervision"
  Abstract: A major obstacle in instance segmentation is that existing methods often need many per-pixel labels in order to be effective. These labels require large human effort and for certain applications, such labels are not readily available. To address this limitation, we propose a novel framework that can effectively train with image-level labels, which are significantly cheaper to acquire. For instance, one can do an internet search for the term "car" and obtain many images where a car is present with minimal effort. Our framework consists of two stages: (1) train a classifier to generate pseudo masks for the objects of interest; (2) train a fully supervised Mask R-CNN on these pseudo masks. Our two main contribution are proposing a pipeline that is simple to implement and is amenable to different segmentation methods; and achieves new state-of-the-art results for this problem setup. Our results are based on evaluating our method on PASCAL VOC 2012, a standard dataset for weakly supervised methods, where we demonstrate major performance gains compared to existing methods with respect to mean average precision.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised instance segmentation by deep community learning"):
  with members:
    - "weakly supervised learning of instance segmentation with inter pixel relations"
    - "weakly supervised object detection with segmentation collaboration"
    - "frame to frame aggregation of active regions in web videos for weakly supervised semantic segmentation"
    - "min entropy latent model for weakly supervised object detection"
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "object counting and instance segmentation with image level supervision"
    - "weakly supervised instance segmentation using class peak response"
    - "group normalization"

Connected through hyperedge HE-2 (induced by source paper "towards partial supervision for generic object counting in natural scenes"):
  with members:
    - "weakly supervised learning of instance segmentation with inter pixel relations"
    - "learning spatial awareness to improve crowd counting"
    - "learning from synthetic data for crowd counting in the wild"
    - "object counting and instance segmentation with image level supervision"
    - "from open set to closed set counting objects by spatial divide and conquer"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "where are the blobs counting by localization with point supervision"
    - "context aware crowd counting"

Question: Please tell me which class the center node 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)