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: "resolution limits on visual speech recognition"
  Abstract: Visual-only speech recognition is dependent upon a number of factors that can be difficult to control, such as: lighting; identity; motion; emotion and expression. But some factors, such as video resolution are controllable, so it is surprising that there is not yet a systematic study of the effect of resolution on lip-reading. Here we use a new data set, the Rosetta Raven data, to train and test recognizers so we can measure the affect of video resolution on recognition accuracy. We conclude that, contrary to common practice, resolution need not be that great for automatic lip-reading. However it is highly unlikely that automatic lip-reading can work reliably when the distance between the bottom of the lower lip and the top of the upper lip is less than four pixels at rest.

Connected through hyperedge HE-1 (induced by source paper "can we read speech beyond the lips rethinking roi selection for deep visual speech recognition"):
  with members:
    - "a cascade sequence to sequence model for chinese mandarin lip reading"
    - "deep audio visual speech recognition"
    - "end to end audiovisual speech recognition"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "lcanet end to end lipreading with cascaded attention ctc"
    - "lrw 1000 a naturally distributed large scale benchmark for lip reading in the wild"
    - "pushing the boundaries of audiovisual word recognition using residual networks and lstms"
    - "words can shift dynamically adjusting word representations using nonverbal behaviors"

Connected through hyperedge HE-2 (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"
    - "comparing phonemes and visemes with dnn based lipreading"
    - "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"

Connected through hyperedge HE-3 (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"
    - "some observations on computer lip reading moving from the dream to the reality"
    - "decoding visemes improving machine lipreading"
    - "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-4 (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"
    - "some observations on computer lip reading moving from the dream to the reality"
    - "visual gesture variability between talkers in continuous speech"
    - "lip reading sentences in the wild"

Connected through hyperedge HE-5 (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"
    - "some observations on computer lip reading moving from the dream to the reality"
    - "decoding visemes improving machine lipreading"

Connected through hyperedge HE-6 (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"
    - "some observations on computer lip reading moving from the dream to the reality"
    - "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"
    - "which phoneme to viseme maps best improve visual only computer lip reading"

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)