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: "speaker independent machine lip reading with speaker dependent viseme classifiers"
  Abstract: In machine lip-reading, which is identification of speech from visual-only information, there is evidence to show that visual speech is highly dependent upon the speaker [1]. Here, we use a phoneme-clustering method to form new phoneme-to-viseme maps for both individual and multiple speakers. We use these maps to examine how similarly speakers talk visually. We conclude that broadly speaking, speakers have the same repertoire of mouth gestures, where they differ is in the use of the gestures.

Connected through hyperedge HE-1 (induced by source paper "algorithms that remember model inversion attacks and data protection law"):
  with members:
    - "decision provenance harnessing data flow for accountable systems"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "third party tracking in the mobile ecosystem"
    - "the unintended consequences of overfitting training data inference attacks"
    - "knock knock who s there membership inference on aggregate location data"
    - "machine learning models that remember too much"
    - "stealing machine learning models via prediction apis"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-2 (induced by source paper "dense multimodal fusion for hierarchically joint representation"):
  with members:
    - "multimodal machine learning a survey and taxonomy"
    - "deep multimodal representation learning from temporal data"
    - "deep binary reconstruction for cross modal hashing"
    - "a comprehensive survey on cross modal retrieval"
    - "convolutional two stream network fusion for video action recognition"
    - "spatiotemporal residual networks for video action recognition"
    - "moddrop adaptive multi modal gesture recognition"
    - "deeply coupled auto encoder networks for cross view classification"

Connected through hyperedge HE-3 (induced by source paper "phoneme to viseme mappings"):
  with members:
    - "visual speech recognition aligning terminologies for better understanding"
    - "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-4 (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"
    - "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-5 (induced by source paper "understanding the visual speech signal"):
  with members:
    - "finding phonemes improving machine lip reading"
    - "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-6 (induced by source paper "decoding visemes improving machine lipreading phd thesis"):
  with members:
    - "finding phonemes improving machine lip reading"
    - "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-7 (induced by source paper "visual gesture variability between talkers in continuous speech"):
  with members:
    - "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"

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)