human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "pose2seg detection free human instance segmentation"):
  Abstract: The standard approach to image instance segmentation is to perform the object detection first, and then segment the object from the detection bounding-box. More recently, deep learning methods like Mask R-CNN perform them jointly. However, little research takes into account the uniqueness of the "human" category, which can be well defined by the pose skeleton. Moreover, the human pose skeleton can be used to better distinguish instances with heavy occlusion than using bounding-boxes. In this paper, we present a brand new pose-based instance segmentation framework for humans which separates instances based on human pose, rather than proposal region detection. We demonstrate that our pose-based framework can achieve better accuracy than the state-of-art detection-based approach on the human instance segmentation problem, and can moreover better handle occlusion. Furthermore, there are few public datasets containing many heavily occluded humans along with comprehensive annotations, which makes this a challenging problem seldom noticed by researchers. Therefore, in this paper we introduce a new benchmark "Occluded Human (OCHuman)", which focuses on occluded humans with comprehensive annotations including bounding-box, human pose and instance masks. This dataset contains 8110 detailed annotated human instances within 4731 images. With an average 0.67 MaxIoU for each person, OCHuman is the most complex and challenging dataset related to human instance segmentation. Through this dataset, we want to emphasize occlusion as a challenging problem for researchers to study.

Includes members:
  Member "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "higherhrnet scale aware representation learning for bottom up human pose estimation")
  Member "crowdhuman a benchmark for detecting human in a crowd":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adapted center and scale prediction more stable and more accurate")
  Member "what can help pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "psc net learning part spatial co occurence for occluded pedestrian detection")
  Member "towards accurate multi person pose estimation in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "higherhrnet scale aware representation learning for bottom up human pose estimation")
  Member "cascaded pyramid network for multi person pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "higherhrnet scale aware representation learning for bottom up human pose estimation")
  Member "pose2instance harnessing keypoints for person instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sideinfnet a deep neural network for semi automatic semantic segmentation with side information")
  Member "high quality correspondence and segmentation estimation for dual lens smart phone portraits":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fast portrait segmentation with extremely light weight network")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

Question: Please tell me which class the source paper 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)