human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "speaker independent machine lip reading with speaker dependent viseme classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "algorithms that remember model inversion attacks and data protection law")
  Member "which phoneme to viseme maps best improve visual only computer lip reading":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pushing the boundaries of audiovisual word recognition using residual networks and lstms")
  Member "some observations on computer lip reading moving from the dream to the reality":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "phoneme to viseme mappings")
  Member "decoding visemes improving machine lipreading":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "large scale visual speech recognition")
  Member "resolution limits on visual speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "can we read speech beyond the lips rethinking roi selection for deep visual speech recognition")

Question: Please tell me which class the source paper 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)