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 "end to end visual speech recognition for small scale datasets"):
  Abstract: Traditional visual speech recognition systems consist of two stages, feature extraction and classification. Recently, several deep learning approaches have been presented which automatically extract features from the mouth images and aim to replace the feature extraction stage. However, research on joint learning of features and classification remains limited. In addition, most of the existing methods require large amounts of data in order to achieve state-of-the-art performance, otherwise they under-perform. In this work, we present an end-to-end visual speech recognition system based on fully-connected layers and Long-Short Memory (LSTM) networks which is suitable for small-scale datasets. The model consists of two streams which extract features directly from the mouth and difference images, respectively. The temporal dynamics in each stream are modelled by a Bidirectional LSTM (BLSTM) and the fusion of the two streams takes place via another BLSTM. An absolute improvement of 0.6%, 3.4%, 3.9%, 11.4% over the state-of-the-art is reported on the OuluVS2, CUAVE, AVLetters and AVLetters2 databases, respectively.

Includes members:
  Member "large scale visual speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lipreading using temporal convolutional networks")
  Member "end to end audiovisual speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lipreading using temporal convolutional networks")
  Member "end to end audiovisual fusion with lstms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "from speech chain to multimodal chain leveraging cross modal data augmentation for semi supervised learning")
  Member "lipnet end to end sentence level lipreading":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "can we read speech beyond the lips rethinking roi selection for deep visual speech recognition")
  Member "end to end visual speech recognition with lstms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lipreading using temporal convolutional networks")
  Member "end to end multi view lipreading":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lipreading using temporal convolutional networks")
  Member "combining residual networks with lstms for lipreading":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lipreading using temporal convolutional networks")
  Member "lipreading with long short term memory":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "lipreading using temporal convolutional networks")

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)