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: "a survey on recent advances of computer vision algorithms for egocentric video"
  Abstract: Recent technological advances have made lightweight, head mounted cameras both practical and affordable and products like Google Glass show first approaches to introduce the idea of egocentric (first-person) video to the mainstream. Interestingly, the computer vision community has only recently started to explore this new domain of egocentric vision, where research can roughly be categorized into three areas: Object recognition, activity detection/recognition, video summarization. In this paper, we try to give a broad overview about the different problems that have been addressed and collect and compare evaluation results. Moreover, along with the emergence of this new domain came the introduction of numerous new and versatile benchmark datasets, which we summarize and compare as well.

Connected through hyperedge HE-1 (induced by source paper "toward storytelling from visual lifelogging an overview"):
  with members:
    - "r clustering for egocentric video segmentation"
    - "semantic summarization of egocentric photo stream events"
    - "multi face tracking by extended bag of tracklets in egocentric photo streams"
    - "f formation detection individuating free standing conversational groups in images"
    - "leveraging context to support automated food recognition in restaurants"
    - "predicting daily activities from egocentric images using deep learning"
    - "sequence to sequence video to text"
    - "egocentric field of view localization using first person point of view devices"

Connected through hyperedge HE-2 (induced by source paper "egocentric hand detection via dynamic region growing"):
  with members:
    - "driver hand localization and grasp analysis a vision based real time approach"
    - "joint hand detection and rotation estimation by using cnn"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "the evolution of first person vision methods a survey"
    - "an egocentric look at video photographer identity"

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)