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 using pca networks and lstms in a tandem gmm hmm system"
  Abstract: Automatic visual speech recognition is an interesting problem in pattern recognition especially when audio data is noisy or not readily available. It is also a very challenging task mainly because of the lower amount of information in the visual articulations compared to the audible utterance. In this work, principle component analysis is applied to the image patches — extracted from the video data — to learn the weights of a two-stage convolutional network. Block histograms are then extracted as the unsupervised learning features. These features are employed to learn a recurrent neural network with a set of long short-term memory cells to obtain spatiotemporal features. Finally, the obtained features are used in a tandem GMM-HMM system for speech recognition. Our results show that the proposed method has outperformed the baseline techniques applied to the OuluVS2 audiovisual database for phrase recognition with the frontal view cross-validation and testing sentence correctness reaching 79% and 73%, respectively, as compared to the baseline of 74% on cross-validation.

Connected through hyperedge HE-1 (induced by source paper "face mask extraction in video sequence"):
  with members:
    - "bringing background into the foreground making all classes equal in weakly supervised video semantic segmentation"
    - "end to end semantic face segmentation with conditional random fields as convolutional recurrent and adversarial networks"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "clockwork convnets for video semantic segmentation"
    - "one shot video object segmentation"
    - "semi supervised domain adaptation for weakly labeled semantic video object segmentation"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "end to end visual speech recognition for small scale datasets"):
  with members:
    - "large scale visual speech recognition"
    - "end to end audiovisual speech recognition"
    - "end to end audiovisual fusion with lstms"
    - "lipnet end to end sentence level lipreading"
    - "end to end visual speech recognition with lstms"
    - "end to end multi view lipreading"
    - "combining residual networks with lstms for lipreading"
    - "lipreading with long short term memory"

Connected through hyperedge HE-3 (induced by source paper "harnessing ai for speech reconstruction using multi view silent video feed"):
  with members:
    - "improved speech reconstruction from silent video"
    - "lip2audspec speech reconstruction from silent lip movements video"
    - "end to end multi view lipreading"
    - "lipreading with long short term memory"
    - "visually indicated sounds"

Connected through hyperedge HE-4 (induced by source paper "end to end multi view lipreading"):
  with members:
    - "lipnet end to end sentence level lipreading"
    - "end to end visual speech recognition with lstms"
    - "lipreading with long short term memory"
    - "lip reading sentences in the wild"
    - "adam a method for stochastic optimization"

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)