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: "lip localization and viseme classification for visual speech recognition"
  Abstract: The need for an automatic lip-reading system is ever increasing. Infact, today, extraction and reliable analysis of facial movements make up an important part in many multimedia systems such as videoconference, low communication systems, lip-reading systems. In addition, visual information is imperative among people with special needs. We can imagine, for example, a dependent person ordering a machine with an easy lip movement or by a simple syllable pronunciation. Moreover, people with hearing problems compensate for their special needs by lip-reading as well as listening to the person with whome they are talking. We present in this paper a new approach to automatically localize lip feature points in a speaker’s face and to carry out a spatial-temporal tracking of these points. The extracted visual information is then classified in order to recognize the uttered viseme (visual phoneme). We have developed our Automatic Lip Feature Extraction prototype (ALiFE). Experiments revealed that our system recognizes 72.73% of French Vowels uttered by multiple speakers (female and male) under natural conditions.

Connected through hyperedge HE-1 (induced by source paper "3d convolutional neural networks for cross audio visual matching recognition"):
  with members:
    - "text independent speaker verification using 3d convolutional neural networks"
    - "lipreading with long short term memory"
    - "deep multimodal learning for audio visual speech recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "building high level features using large scale unsupervised learning"

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)