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: "object detection free instance segmentation with labeling transformations"
  Abstract: Instance segmentation has attracted recent attention in computer vision and existing methods in this domain mostly have an object detection stage. In this paper, we study the intrinsic challenge of the instance segmentation problem, the presence of a quotient space (swapping the labels of different instances leads to the same result), and propose new methods that are object proposal- and object detection- free. We propose three alternative methods, namely pixel-based affinity mapping, superpixel-based affinity learning, and boundary-based component segmentation, all focusing on performing labeling transformations to cope with the quotient space problem. By adopting fully convolutional neural networks (FCN) like models, our framework attains competitive results on both the PASCAL dataset (object-centric) and the Gland dataset (texture-centric), which the existing methods are not able to do. Our work also has the advantages in its transparency, simplicity, and being all segmentation based.

Connected through hyperedge HE-1 (induced by source paper "learning instance occlusion for panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "an end to end network for panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "adaptis adaptive instance selection network"
    - "mask scoring r cnn"
    - "panoptic feature pyramid networks"
    - "attention guided unified network for panoptic segmentation"
    - "learning to fuse things and stuff"

Connected through hyperedge HE-2 (induced by source paper "mask scoring r cnn"):
  with members:
    - "ccnet criss cross attention for semantic segmentation"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "acquisition of localization confidence for accurate object detection"
    - "object detection in videos by high quality object linking"
    - "object detection in videos by short and long range object linking"
    - "segmentation aware convolutional networks using local attention masks"
    - "improving object localization with fitness nms and bounded iou loss"
    - "deformable convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"):
  with members:
    - "path aggregation network for instance segmentation"
    - "soft nms improving object detection with one line of code"
    - "data distillation towards omni supervised learning"
    - "megdet a large mini batch object detector"
    - "towards accurate multi person pose estimation in the wild"
    - "cascaded pyramid network for multi person pose estimation"
    - "masklab instance segmentation by refining object detection with semantic and direction features"
    - "semantic instance segmentation via deep metric learning"

Connected through hyperedge HE-4 (induced by source paper "affinity derivation and graph merge for instance segmentation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "deformable convolutional networks"
    - "masklab instance segmentation by refining object detection with semantic and direction features"
    - "stacked deconvolutional network for semantic segmentation"
    - "semantic instance segmentation via deep metric learning"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-5 (induced by source paper "learning to cluster for proposal free instance segmentation"):
  with members:
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "semantic instance segmentation with a discriminative loss function"
    - "spatial as deep spatial cnn for traffic scene understanding"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "instance sensitive fully convolutional networks"
    - "pixel level encoding and depth layering for instance level semantic labeling"
    - "boundary aware instance segmentation"

Connected through hyperedge HE-6 (induced by source paper "masklab instance segmentation by refining object detection with semantic and direction features"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "deformable convolutional networks"
    - "understanding convolution for semantic segmentation"
    - "semantic instance segmentation via deep metric learning"
    - "semantic instance segmentation with a discriminative loss function"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"

Connected through hyperedge HE-7 (induced by source paper "gland instance segmentation using deep multichannel neural networks"):
  with members:
    - "instance sensitive fully convolutional networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "gland instance segmentation by deep multichannel side supervision"
    - "learning to refine object segments"
    - "gland segmentation in colon histology images the glas challenge contest"
    - "dcan deep contour aware networks for accurate gland segmentation"
    - "learning to segment object candidates"
    - "holistically nested edge detection"

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)