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 and semi supervised panoptic segmentation"
  Abstract: We present a weakly supervised model that jointly performs both semantic- and instance-segmentation -- a particularly relevant problem given the substantial cost of obtaining pixel-perfect annotation for these tasks. In contrast to many popular instance segmentation approaches based on object detectors, our method does not predict any overlapping instances. Moreover, we are able to segment both "thing" and "stuff" classes, and thus explain all the pixels in the image. "Thing" classes are weakly-supervised with bounding boxes, and "stuff" with image-level tags. We obtain state-of-the-art results on Pascal VOC, for both full and weak supervision (which achieves about 95% of fully-supervised performance). Furthermore, we present the first weakly-supervised results on Cityscapes for both semantic- and instance-segmentation. Finally, we use our weakly supervised framework to analyse the relationship between annotation quality and predictive performance, which is of interest to dataset creators.

Connected through hyperedge HE-1 (induced by source paper "towards bounding box free panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "panoptic edge detection"
    - "upsnet a unified panoptic segmentation network"
    - "fast panoptic segmentation network"
    - "adaptis adaptive instance selection network"
    - "ssap single shot instance segmentation with affinity pyramid"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"

Connected through hyperedge HE-2 (induced by source paper "realizing pixel level semantic learning in complex driving scenes based on only one annotated pixel per class"):
  with members:
    - "confidence regularized self training"
    - "box driven class wise region masking and filling rate guided loss for weakly supervised semantic segmentation"
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "bringing background into the foreground making all classes equal in weakly supervised video semantic segmentation"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"

Connected through hyperedge HE-3 (induced by source paper "a survey on deep learning of small sample in biomedical image analysis"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "generative adversarial network in medical imaging a review"
    - "nnu net breaking the spell on successful medical image segmentation"
    - "constrained cnn losses for weakly supervised segmentation"
    - "angle closure detection in anterior segment oct based on multilevel deep network"
    - "chestnet a deep neural network for classification of thoracic diseases on chest radiography"
    - "boxnet deep learning based biomedical image segmentation using boxes only annotation"

Connected through hyperedge HE-4 (induced by source paper "panoptic feature pyramid networks"):
  with members:
    - "panoptic segmentation"
    - "group normalization"
    - "learning to fuse things and stuff"
    - "panoptic segmentation with a joint semantic and instance segmentation network"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"
    - "triply supervised decoder networks for joint detection and segmentation"
    - "non local neural networks"

Connected through hyperedge HE-5 (induced by source paper "ssap single shot instance segmentation with affinity pyramid"):
  with members:
    - "panoptic segmentation"
    - "deeperlab single shot image parser"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "affinity derivation and graph merge for instance segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "path aggregation network for instance segmentation"
    - "focal loss for dense object detection"

Connected through hyperedge HE-6 (induced by source paper "seamless scene segmentation"):
  with members:
    - "an end to end network for panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "deeperlab single shot image parser"
    - "panoptic feature pyramid networks"
    - "learning to fuse things and stuff"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"

Connected through hyperedge HE-7 (induced by source paper "an end to end network for panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "attention guided unified network for panoptic segmentation"
    - "taskonomy disentangling task transfer learning"
    - "panoptic segmentation with a joint semantic and instance segmentation network"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"

Connected through hyperedge HE-8 (induced by source paper "spatialflow bridging all tasks for panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "an end to end network for panoptic segmentation"
    - "region proposal by guided anchoring"
    - "upsnet a unified panoptic segmentation network"
    - "box driven class wise region masking and filling rate guided loss for weakly supervised semantic segmentation"
    - "deeperlab single shot image parser"
    - "mmdetection open mmlab detection toolbox and benchmark"

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)