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: "left right hand segmentation in egocentric videos"
  Abstract: Wearable cameras allow people to record their daily activities from a user-centered (First Person Vision) perspective. Due to their favorable location, wearable cameras frequently capture the hands of the user, and may thus represent a promising user-machine interaction tool for different applications. Existent First Person Vision methods handle hand segmentation as a background-foreground problem, ignoring two important facts: i) hands are not a single "skin-like" moving element, but a pair of interacting cooperative entities, ii) close hand interactions may lead to hand-to-hand occlusions and, as a consequence, create a single hand-like segment. These facts complicate a proper understanding of hand movements and interactions. Our approach extends traditional background-foreground strategies, by including a hand-identification step (left-right) based on a Maxwell distribution of angle and position. Hand-to-hand occlusions are addressed by exploiting temporal superpixels. The experimental results show that, in addition to a reliable left/right hand-segmentation, our approach considerably improves the traditional background-foreground hand-segmentation.

Connected through hyperedge HE-1 (induced by source paper "analysis of the hands in egocentric vision a survey"):
  with members:
    - "an effective and efficient method for detecting hands in egocentric videos for rehabilitation applications"
    - "recurrent u net for resource constrained segmentation"
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "domain specific priors and meta learning for low shot first person action recognition"
    - "egocentric hand track and object based human action recognition"
    - "egocentric video a new tool for capturing hand use of individuals with spinal cord injury at home"
    - "scaling egocentric vision the epic kitchens dataset"
    - "analysis of hand segmentation in the wild"

Connected through hyperedge HE-2 (induced by source paper "egocentric hand track and object based human action recognition"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "multi fiber networks for video recognition"
    - "yolov3 an incremental improvement"
    - "object level visual reasoning in videos"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "human action recognition pose based attention draws focus to hands"
    - "first person action object detection with egonet"
    - "simple online and realtime tracking"

Connected through hyperedge HE-3 (induced by source paper "beyond one glance gated recurrent architecture for hand segmentation"):
  with members:
    - "group normalization"
    - "analysis of hand segmentation in the wild"
    - "beyond the pixel wise loss for topology aware delineation"
    - "recurrent fully convolutional networks for video segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "delving deeper into convolutional networks for learning video representations"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "recurrent instance segmentation"

Connected through hyperedge HE-4 (induced by source paper "analysis of hand segmentation in the wild"):
  with members:
    - "learning to estimate 3d hand pose from single rgb images"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "first person hand action benchmark with rgb d videos and 3d hand pose annotations"
    - "hand segmentation for hand object interaction from depth map"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "gesture based bootstrapping for egocentric hand segmentation"):
  with members:
    - "parsenet looking wider to see better"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "unsupervised understanding of location and illumination changes in egocentric videos"):
  with members:
    - "the evolution of first person vision methods a survey"

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.HC(Human-Computer Interaction)