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: "interactive full image segmentation by considering all regions jointly"
  Abstract: We address interactive full image annotation, where the goal is to accurately segment all object and stuff regions in an image. We propose an interactive, scribble-based annotation framework which operates on the whole image to produce segmentations for all regions. This enables sharing scribble corrections across regions, and allows the annotator to focus on the largest errors made by the machine across the whole image. To realize this, we adapt Mask-RCNN into a fast interactive segmentation framework and introduce an instance-aware loss measured at the pixel-level in the full image canvas, which lets predictions for nearby regions properly compete for space. Finally, we compare to interactive single object segmentation on the COCO panoptic dataset. We demonstrate that our interactive full image segmentation approach leads to a 5% IoU gain, reaching 90% IoU at a budget of four extreme clicks and four corrective scribbles per region.

Connected through hyperedge HE-1 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to segment via cut and paste"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "visual affordance and function understanding a survey"
    - "collaborative annotation of semantic objects in images with multi granularity supervisions"

Connected through hyperedge HE-2 (induced by source paper "f brs rethinking backpropagating refinement for interactive segmentation"):
  with members:
    - "lvis a dataset for large vocabulary instance segmentation"
    - "large scale interactive object segmentation with human annotators"
    - "bag of tricks for image classification with convolutional neural networks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "dynamic zoom in network for fast object detection in large images"
    - "deep interactive object selection"
    - "texture synthesis using convolutional neural networks"
    - "adaptive object detection using adjacency and zoom prediction"

Connected through hyperedge HE-3 (induced by source paper "continuous adaptation for interactive object segmentation by learning from corrections"):
  with members:
    - "incremental learning techniques for semantic segmentation"
    - "large scale interactive object segmentation with human annotators"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "task free continual learning"
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "interactive video object segmentation in the wild"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "iteratively trained interactive segmentation"

Connected through hyperedge HE-4 (induced by source paper "panoptic image annotation with a collaborative assistant"):
  with members:
    - "panoptic segmentation"
    - "large scale interactive object segmentation with human annotators"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "iteratively trained interactive segmentation"
    - "guide me interacting with deep networks"
    - "fluid annotation a human machine collaboration interface for full image annotation"
    - "blazingly fast video object segmentation with pixel wise metric learning"

Connected through hyperedge HE-5 (induced by source paper "interactive segmentation of medical images through fully convolutional neural networks"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "nnu net self adapting framework for u net based medical image segmentation"
    - "deep extreme cut from extreme points to object segmentation"
    - "dltk state of the art reference implementations for deep learning on medical images"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

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)