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: "visual gesture variability between talkers in continuous speech"
  Abstract: Recent adoption of deep learning methods to the field of machine lipreading research#R##N#gives us two options to pursue to improve system performance. Either, we develop endto-#R##N#end systems holistically or, we experiment to further our understanding of the visual#R##N#speech signal. The latter option is more difficult but this knowledge would enable researchers#R##N#to both improve systems and apply the new knowledge to other domains such#R##N#as speech therapy.#R##N#One challenge in lipreading systems is the correct labeling of the classifiers. These#R##N#labels map an estimated function between visemes on the lips and the phonemes uttered.#R##N#Here we ask if such maps are speaker-dependent? Prior work investigated isolated#R##N#word recognition from speaker-dependent (SD) visemes, we extend this to continuous#R##N#speech. Benchmarked against SD results, and the isolated words performance, we test#R##N#with RMAV dataset speakers and observe that with continuous speech, the trajectory#R##N#between visemes has a greater negative effect on the speaker differentiation.

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

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)