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: "which phoneme to viseme maps best improve visual only computer lip reading"
  Abstract: A critical assumption of all current visual speech recognition systems is that there are visual speech units called visemes which can be mapped to units of acoustic speech, the phonemes. Despite there being a number of published maps it is infrequent to see the effectiveness of these tested, particularly on visual-only lip-reading (many works use audio-visual speech). Here we examine 120 mappings and consider if any are stable across talkers. We show a method for devising maps based on phoneme confusions from an automated lip-reading system, and we present new mappings that show improvements for individual talkers.

Connected through hyperedge HE-1 (induced by source paper "pushing the boundaries of audiovisual word recognition using residual networks and lstms"):
  with members:
    - "deep lip reading a comparison of models and an online application"
    - "the conversation deep audio visual speech enhancement"
    - "advancing acoustic to word ctc model"
    - "end to end audiovisual speech recognition"
    - "zero shot keyword spotting for visual speech recognition in the wild"
    - "improving speaker independent lipreading with domain adversarial training"
    - "attention is all you need"
    - "deep word embeddings for visual speech recognition"

Connected through hyperedge HE-2 (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"
    - "some observations on computer lip reading moving from the dream to the reality"
    - "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-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"
    - "some observations on computer lip reading moving from the dream to the reality"
    - "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"
    - "some observations on computer lip reading moving from the dream to the reality"
    - "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"
    - "some observations on computer lip reading moving from the dream to the reality"
    - "decoding visemes improving machine lipreading"
    - "resolution limits on visual speech recognition"

Connected through hyperedge HE-6 (induced by source paper "comparing phonemes and visemes with dnn based lipreading"):
  with members:
    - "visual speech recognition aligning terminologies for better understanding"
    - "decoding visemes improving machine lipreading"

Connected through hyperedge HE-7 (induced by source paper "decoding visemes improving machine lipreading"):
  with members:
    - "finding phonemes improving machine lip reading"
    - "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)