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: "proposal free network for instance level object segmentation"
  Abstract: Instance-level object segmentation is an important yet under-explored task. The few existing studies are almost all based on region proposal methods to extract candidate segments and then utilize object classification to produce final results. Nonetheless, generating accurate region proposals itself is quite challenging. In this work, we propose a Proposal-Free Network (PFN ) to address the instance-level object segmentation problem, which outputs the instance numbers of different categories and the pixel-level information on 1) the coordinates of the instance bounding box each pixel belongs to, and 2) the confidences of different categories for each pixel, based on pixel-to-pixel deep convolutional neural network. All the outputs together, by using any off-the-shelf clustering method for simple post-processing, can naturally generate the ultimate instance-level object segmentation results. The whole PFN can be easily trained in an end-to-end way without the requirement of a proposal generation stage. Extensive evaluations on the challenging PASCAL VOC 2012 semantic segmentation benchmark demonstrate that the proposed PFN solution well beats the state-of-the-arts for instance-level object segmentation. In particular, the $AP^r$ over 20 classes at 0.5 IoU reaches 58.7% by PFN, significantly higher than 43.8% and 46.3% by the state-of-the-art algorithms, SDS [9] and [16], respectively.

Connected through hyperedge HE-1 (induced by source paper "deeperlab single shot image parser"):
  with members:
    - "panoptic segmentation"
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "affinity derivation and graph merge for instance segmentation"
    - "unified perceptual parsing for scene understanding"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"

Connected through hyperedge HE-2 (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"
    - "weakly and semi supervised panoptic segmentation"

Connected through hyperedge HE-3 (induced by source paper "instance segmentation with point supervision"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "object counting and instance segmentation with image level supervision"
    - "weakly supervised instance segmentation using class peak response"
    - "learning to segment via cut and paste"
    - "where are the blobs counting by localization with point supervision"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "on the importance of visual context for data augmentation in scene understanding"

Connected through hyperedge HE-4 (induced by source paper "yolact better real time instance segmentation"):
  with members:
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "mask scoring r cnn"
    - "yolact real time instance segmentation"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "yolov3 an incremental improvement"
    - "deformable convnets v2 more deformable better results"
    - "path aggregation network for instance segmentation"
    - "segmentation aware convolutional networks using local attention masks"

Connected through hyperedge HE-5 (induced by source paper "yolact real time instance segmentation"):
  with members:
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "mask scoring r cnn"
    - "yolov3 an incremental improvement"
    - "path aggregation network for instance segmentation"
    - "segmentation aware convolutional networks using local attention masks"
    - "focal loss for dense object detection"
    - "blitznet a real time deep network for scene understanding"
    - "icnet for real time semantic segmentation on high resolution images"

Connected through hyperedge HE-6 (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-7 (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-8 (induced by source paper "embedmask embedding coupling for one stage instance segmentation"):
  with members:
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "polarmask single shot instance segmentation with polar representation"
    - "mask scoring r cnn"
    - "tensormask a foundation for dense object segmentation"
    - "fcos fully convolutional one stage object detection"
    - "yolact real time instance segmentation"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "path aggregation network for instance 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)