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: "egocentric video biometrics"
  Abstract: Egocentric cameras are being worn by an increasing number of users, among them many security forces worldwide. GoPro cameras already penetrated the mass market, and Google Glass may follow soon. As head-worn cameras do not capture the face and body of the wearer, it may seem that the anonymity of the wearer can be preserved even when the video is publicly distributed. #R##N#We show that motion features in egocentric video provide biometric information, and the identity of the user can be reliably determined from a few seconds of video captured when the user is walking. The proposed method achieves more than 90% identification accuracy in cases where the random success rate is only 3%. #R##N#Applications may include theft prevention of wearable cameras by locking the camera when not worn by its lawful owner. This work can also provide the first steps towards searching on video sharing services (e.g. YouTube) for egocentric videos shot by a specific person. An important message in this paper is that people should be aware that sharing egocentric video will compromise their anonymity, even when their face is not visible.

Connected through hyperedge HE-1 (induced by source paper "compact cnn for indexing egocentric videos"):
  with members:
    - "ego surfing first person videos"
    - "egosampling fast forward and stereo for egocentric videos"
    - "pooled motion features for first person videos"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "caffe convolutional architecture for fast feature embedding"
    - "modeep a deep learning framework using motion features for human pose estimation"
    - "finding action tubes"

Connected through hyperedge HE-2 (induced by source paper "personalized image based user authentication using wearable cameras"):
  with members:
    - "predicting daily activities from egocentric images using deep learning"
    - "recognizing activities of daily living with a wrist mounted camera"
    - "hmog new behavioral biometric features for continuous authentication of smartphone users"
    - "pupil an open source platform for pervasive eye tracking and mobile gaze based interaction"

Connected through hyperedge HE-3 (induced by source paper "trajectory aligned features for first person action recognition"):
  with members:
    - "egosampling fast forward and stereo for egocentric videos"
    - "predicting daily activities from egocentric images using deep learning"
    - "pooled motion features for first person videos"

Connected through hyperedge HE-4 (induced by source paper "the evolution of first person vision methods a survey"):
  with members:
    - "egocentric field of view localization using first person point of view devices"
    - "predicting important objects for egocentric video summarization"
    - "egocentric pose recognition in four lines of code"
    - "human daily activities indexing in videos from wearable cameras for monitoring of patients with dementia diseases"

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)