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: "some observations on computer lip reading moving from the dream to the reality"
  Abstract: In the quest for greater computer lip-reading performance there are a number of tacit assumptions which are either present in the datasets (high resolution for example) or in the methods (recognition of spoken visual units called visemes for example). Here we review these and other assumptions and show the surprising result that computer lip-reading is not heavily constrained by video resolution, pose, lighting and other practical factors. However, the working assumption that visemes, which are the visual equivalent of phonemes, are the best unit for recognition does need further examination. We conclude that visemes, which were defined over a century ago, are unlikely to be optimal for a modern computer lip-reading system.

Connected through hyperedge HE-1 (induced by source paper "phoneme to viseme mappings"):
  with members:
    - "visual speech recognition aligning terminologies for better understanding"
    - "speaker independent machine lip reading with speaker dependent viseme classifiers"
    - "which phoneme to viseme maps best improve visual only computer lip reading"
    - "decoding visemes improving machine lipreading"
    - "resolution limits on visual speech recognition"
    - "lipreading with long short term memory"
    - "automatic facial expression recognition using features of salient facial patches"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-2 (induced by source paper "visual speech recognition aligning terminologies for better understanding"):
  with members:
    - "comparing phonemes and visemes with dnn based lipreading"
    - "finding phonemes improving machine lip reading"
    - "speaker independent machine lip reading with speaker dependent viseme classifiers"
    - "resolution limits on visual speech recognition"
    - "visual gesture variability between talkers in continuous speech"
    - "lip reading sentences in the wild"

Connected through hyperedge HE-3 (induced by source paper "understanding the visual speech signal"):
  with members:
    - "finding phonemes improving machine lip reading"
    - "speaker independent machine lip reading with speaker dependent viseme classifiers"
    - "which phoneme to viseme maps best improve visual only computer lip reading"
    - "decoding visemes improving machine lipreading"
    - "visual gesture variability between talkers in continuous speech"

Connected through hyperedge HE-4 (induced by source paper "decoding visemes improving machine lipreading phd thesis"):
  with members:
    - "finding phonemes improving machine lip reading"
    - "speaker independent machine lip reading with speaker dependent viseme classifiers"
    - "which phoneme to viseme maps best improve visual only computer lip reading"
    - "decoding visemes improving machine lipreading"
    - "resolution limits on visual speech recognition"

Connected through hyperedge HE-5 (induced by source paper "visual gesture variability between talkers in continuous speech"):
  with members:
    - "speaker independent machine lip reading with speaker dependent viseme classifiers"
    - "which phoneme to viseme maps best improve visual only computer lip reading"
    - "decoding visemes improving machine lipreading"
    - "resolution limits on visual speech 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.CV(Computer Vision and Pattern Recognition)