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: "comparing phonemes and visemes with dnn based lipreading"
  Abstract: There is debate if phoneme or viseme units are the most effective for a lipreading#R##N#system. Some studies use phoneme units even though phonemes describe unique short#R##N#sounds; other studies tried to improve lipreading accuracy by focusing on visemes with#R##N#varying results. We compare the performance of a lipreading system by modeling visual#R##N#speech using either 13 viseme or 38 phoneme units. We report the accuracy of our#R##N#system at both word and unit levels. The evaluation task is large vocabulary continuous#R##N#speech using the TCD-TIMIT corpus. We complete our visual speech modeling via#R##N#hybrid DNN-HMMs and our visual speech decoder is aWeighted Finite-State Transducer#R##N#(WFST). We use DCT and Eigenlips as a representation of mouth ROI image. The#R##N#phoneme lipreading system word accuracy outperforms the viseme based system word#R##N#accuracy. However, the phoneme system achieved lower accuracy at the unit level which#R##N#shows the importance of the dictionary for decoding classification outputs into words.

Connected through hyperedge HE-1 (induced by source paper "the speaker independent lipreading play off a survey of lipreading machines"):
  with members:
    - "large scale visual speech recognition"
    - "audio visual speech recognition with a hybrid ctc attention architecture"
    - "visual speech recognition aligning terminologies for better understanding"
    - "improving speaker independent lipreading with domain adversarial training"
    - "lipnet end to end sentence level lipreading"
    - "decoding visemes improving machine lipreading"
    - "combining residual networks with lstms for lipreading"
    - "resolution limits on visual speech recognition"

Connected through hyperedge HE-2 (induced by source paper "visual speech recognition aligning terminologies for better understanding"):
  with members:
    - "finding phonemes improving machine lip reading"
    - "speaker independent machine lip reading with speaker dependent viseme classifiers"
    - "some observations on computer lip reading moving from the dream to the reality"
    - "resolution limits on visual speech recognition"
    - "visual gesture variability between talkers in continuous speech"
    - "lip reading sentences in the wild"

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)