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: "visual passwords using automatic lip reading"
  Abstract: This paper presents a visual passwords system to increase security. The system depends mainly on recognizing the speaker using the visual speech signal alone. The proposed scheme works in two stages: setting the visual password stage and the verification stage. At the setting stage the visual passwords system request the user to utter a selected password, a video recording of the user face is captured, and processed by a special words-based VSR system which extracts a sequence of feature vectors. In the verification stage, the same procedure is executed, the features will be sent to be compared with the stored visual password. The proposed scheme has been evaluated using a video database of 20 different speakers (10 females and 10 males), and 15 more males in another video database with different experiment sets. The evaluation has proved the system feasibility, with average error rate in the range of 7.63% to 20.51% at the worst tested scenario, and therefore, has potential to be a practical approach with the support of other conventional authentication methods such as the use of usernames and passwords.

Connected through hyperedge HE-1 (induced by source paper "combining multiple views for visual speech recognition"):
  with members:
    - "end to end visual speech recognition with lstms"
    - "lipreading with long short term memory"
    - "lip reading sentences in the wild"
    - "deep multimodal learning for audio visual speech recognition"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "visual speech recognition using pca networks and lstms in a tandem gmm hmm system"):
  with members:
    - "lipreading with long short term memory"
    - "deep multimodal learning for audio visual speech recognition"
    - "pcanet a simple deep learning baseline for image classification"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "solving the problem of the k parameter in the knn classifier using an ensemble learning approach"):
  with members:
    - "visual speech recognition"
    - "survey of nearest neighbor techniques"

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)