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 "can we read speech beyond the lips rethinking roi selection for deep visual speech recognition"):
  Abstract: Recent advances in deep learning have heightened interest among researchers in the field of visual speech recognition (VSR). Currently, most existing methods equate VSR with automatic lip reading, which attempts to recognise speech by analysing lip motion. However, human experience and psychological studies suggest that we do not always fix our gaze at each other's lips during a face-to-face conversation, but rather scan the whole face repetitively. This inspires us to revisit a fundamental yet somehow overlooked problem: can VSR models benefit from reading extraoral facial regions, i.e. beyond the lips? In this paper, we perform a comprehensive study to evaluate the effects of different facial regions with state-of-the-art VSR models, including the mouth, the whole face, the upper face, and even the cheeks. Experiments are conducted on both word-level and sentence-level benchmarks with different characteristics. We find that despite the complex variations of the data, incorporating information from extraoral facial regions, even the upper face, consistently benefits VSR performance. Furthermore, we introduce a simple yet effective method based on Cutout to learn more discriminative features for face-based VSR, hoping to maximise the utility of information encoded in different facial regions. Our experiments show obvious improvements over existing state-of-the-art methods that use only the lip region as inputs, a result we believe would probably provide the VSR community with some new and exciting insights.

Includes members:
  Member "a cascade sequence to sequence model for chinese mandarin lip reading":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hearing lips improving lip reading by distilling speech recognizers")
  Member "deep audio visual speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lipreading using temporal convolutional networks")
  Member "end to end audiovisual speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lipreading using temporal convolutional networks")
  Member "looking to listen at the cocktail party a speaker independent audio visual model for speech separation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "lcanet end to end lipreading with cascaded attention ctc":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mutual information maximization for effective lip reading")
  Member "lrw 1000 a naturally distributed large scale benchmark for lip reading in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lipreading using temporal convolutional networks")
  Member "pushing the boundaries of audiovisual word recognition using residual networks and lstms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mutual information maximization for effective lip reading")
  Member "words can shift dynamically adjusting word representations using nonverbal behaviors":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "factorized multimodal transformer for multimodal sequential learning")

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)