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: "weakly supervised semantic labelling and instance segmentation"
  Abstract: Semantic labelling and instance segmentation are two tasks that require particularly costly annotations. Starting from weak supervision in the form of bounding box detection annotations, we propose to recursively train a convnet such that outputs are improved after each iteration. We explore which aspects affect the recursive training, and which is the most suitable box-guided segmentation to use as initialisation. Our results improve significantly over previously reported ones, even when using rectangles as rough initialisation. Overall, our weak supervision approach reaches ~95% of the quality of the fully supervised model, both for semantic labelling and instance segmentation.

Connected through hyperedge HE-1 (induced by source paper "signet ring cell detection with a semi supervised learning framework"):
  with members:
    - "pseudo mask augmented object detection"
    - "deep layer aggregation"
    - "learning to segment every thing"
    - "deepcut object segmentation from bounding box annotations using convolutional neural networks"
    - "simple does it weakly supervised instance and semantic segmentation"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-2 (induced by source paper "environment reconstruction on depth images using generative adversarial networks"):
  with members:
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "semantic road layout understanding by generative adversarial inpainting"
    - "deep depth completion of a single rgb d image"
    - "generative image inpainting with contextual attention"
    - "semantic 3d occupancy mapping through efficient high order crfs"
    - "improved training of wasserstein gans"
    - "depth image inpainting improving low rank matrix completion with low gradient regularization"

Connected through hyperedge HE-3 (induced by source paper "incorporating network built in priors in weakly supervised semantic segmentation"):
  with members:
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "weakly supervised semantic segmentation using motion cues"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "built in foreground background prior for weakly supervised semantic segmentation"
    - "simple does it weakly supervised instance and semantic segmentation"
    - "deep hierarchical parsing for semantic segmentation"
    - "conditional random fields as recurrent neural networks"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "exploiting saliency for object segmentation from image level labels"):
  with members:
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "convexification of learning from constraints"
    - "deep contrast learning for salient object detection"
    - "improving weakly supervised object localization by micro annotation"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "built in foreground background prior for weakly supervised semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

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)