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: "live face de identification in video"
  Abstract: We propose a method for face de-identification that enables fully automatic video modification at high frame rates. The goal is to maximally decorrelate the identity, while having the perception (pose, illumination and expression) fixed. We achieve this by a novel feed-forward encoder-decoder network architecture that is conditioned on the high-level representation of a person's facial image. The network is global, in the sense that it does not need to be retrained for a given video or for a given identity, and it creates natural looking image sequences with little distortion in time.

Connected through hyperedge HE-1 (induced by source paper "towards privacy protection by generating adversarial identity masks"):
  with members:
    - "analyzing and improving the image quality of stylegan"
    - "anonymousnet natural face de identification with measurable privacy"
    - "cosface large margin cosine loss for deep face recognition"
    - "privacy protective gan for face de identification"
    - "arcface additive angular margin loss for deep face recognition"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"
    - "a hybrid model for identity obfuscation by face replacement"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-2 (induced by source paper "deepfakes for medical video de identification privacy protection and diagnostic information preservation"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "vision based gait analysis for senior care"
    - "benchmarking and error diagnosis in multi instance pose estimation"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "convolutional pose machines"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "deep residual learning for image recognition"

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.LG(Machine Learning)