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 "phoneme to viseme mappings"):
  Abstract: Visemes are the visual equivalent of phonemes. Although not precisely defined, a common working definition of a viseme is a set of phonemes which have identical appearance on the lips. Therefore a phoneme falls into one viseme class but a viseme may represent many phonemes: a one-to-many mapping. This mapping introduces ambiguity between phonemes when using viseme classifiers. Not only is this ambiguity damaging to the performance of audio-visual classifiers operating on real expressive speech, there is also considerable choice between possible mappings.In this paper we explore the issue of this choice of viseme-to-phoneme map. We show that there is definite difference in performance between viseme-to-phoneme mappings and explore why some maps appear to work better than others. We also devise a new algorithm for constructing phoneme-to-viseme mappings from labeled speech data. These new visemes, Bear visemes, are shown to perform better than previously known units.

Includes members:
  Member "visual speech recognition aligning terminologies for better understanding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the speaker independent lipreading play off a survey of lipreading machines")
  Member "speaker independent machine lip reading with speaker dependent viseme classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (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-4 (induced by source paper "visual speech recognition aligning terminologies for better understanding")
  Member "decoding visemes improving machine lipreading":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "large scale visual speech recognition")
  Member "resolution limits on visual speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "can we read speech beyond the lips rethinking roi selection for deep visual speech recognition")
  Member "lipreading with long short term memory":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lipreading using temporal convolutional networks")
  Member "automatic facial expression recognition using features of salient facial patches":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to augment expressions for few shot fine grained facial expression 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)