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: "finding phonemes improving machine lip reading"
  Abstract: In machine lip-reading there is continued debate and research around the correct classes to be used for recognition. In this paper we use a structured approach for devising speaker-dependent viseme classes, which enables the creation of a set of phoneme-to-viseme maps where each has a different quantity of visemes ranging from two to 45. Viseme classes are based upon the mapping of articulated phonemes, which have been confused during phoneme recognition, into viseme groups. Using these maps, with the LiLIR dataset, we show the effect of changing the viseme map size in speaker-dependent machine lip-reading, measured by word recognition correctness and so demonstrate that word recognition with phoneme classifiers is not just possible, but often better than word recognition with viseme classifiers. Furthermore, there are intermediate units between visemes and phonemes which are better still.

Connected through hyperedge HE-1 (induced by source paper "visual speech recognition aligning terminologies for better understanding"):
  with members:
    - "comparing phonemes and visemes with dnn based lipreading"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "understanding the visual speech signal"):
  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"
    - "visual gesture variability between talkers in continuous speech"

Connected through hyperedge HE-3 (induced by source paper "decoding visemes improving machine lipreading phd thesis"):
  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"
    - "resolution limits on visual speech recognition"

Connected through hyperedge HE-4 (induced by source paper "decoding visemes improving machine lipreading"):
  with members:
    - "which phoneme to viseme maps best improve visual only computer 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)