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 speech recognition aligning terminologies for better understanding"
  Abstract: We are at an exciting time for machine lipreading. Traditional research stemmed#R##N#from the adaptation of audio recognition systems. But now, the computer vision community#R##N#is also participating. This joining of two previously disparate areas with different#R##N#perspectives on computer lipreading is creating opportunities for collaborations, but in#R##N#doing so the literature is experiencing challenges in knowledge sharing due to multiple#R##N#uses of terms and phrases and the range of methods for scoring results.#R##N#In particular we highlight three areas with the intention to improve communication#R##N#between those researching lipreading; the effects of interchanging between speech reading#R##N#and lipreading; speaker dependence across train, validation, and test splits; and the#R##N#use of accuracy, correctness, errors, and varying units (phonemes, visemes, words, and#R##N#sentences) to measure system performance. We make recommendations as to how we#R##N#can be more consistent.

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"
    - "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"
    - "resolution limits on visual speech recognition"

Connected through hyperedge HE-2 (induced by source paper "phoneme to viseme mappings"):
  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"
    - "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-3 (induced by source paper "comparing phonemes and visemes with dnn based lipreading"):
  with members:
    - "which phoneme to viseme maps best improve visual only computer lip reading"
    - "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)