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: "modality attention for end to end audio visual speech recognition"
  Abstract: Audio-visual speech recognition (AVSR) system is thought to be one of the most promising solutions for robust speech recognition, especially in noisy environment. In this paper, we propose a novel multimodal attention based method for audio-visual speech recognition which could automatically learn the fused representation from both modalities based on their importance. Our method is realized using state-of-the-art sequence-to-sequence (Seq2seq) architectures. Experimental results show that relative improvements from 2% up to 36% over the auditory modality alone are obtained depending on the different signal-to-noise-ratio (SNR). Compared to the traditional feature concatenation methods, our proposed approach can achieve better recognition performance under both clean and noisy conditions. We believe modality attention based end-to-end method can be easily generalized to other multimodal tasks with correlated information.

Connected through hyperedge HE-1 (induced by source paper "harnessing gans for zero shot learning of new classes in visual speech recognition"):
  with members:
    - "low shot learning from imaginary data"
    - "harnessing ai for speech reconstruction using multi view silent video feed"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "end to end multi view lipreading"
    - "lipreading with long short term memory"
    - "matching networks for one shot learning"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "harnessing gans for addition of new classes in vsr"):
  with members:
    - "generative adversarial networks an overview"
    - "low shot learning from imaginary data"
    - "cold start aware user and product attention for sentiment classification"
    - "harnessing ai for speech reconstruction using multi view silent video feed"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "lip2audspec speech reconstruction from silent lip movements video"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "end to end multi view lipreading"

Connected through hyperedge HE-3 (induced by source paper "analyzing utility of visual context in multimodal speech recognition under noisy conditions"):
  with members:
    - "probing the need for visual context in multimodal machine translation"
    - "how2 a large scale dataset for multimodal language understanding"
    - "visual features for context aware speech recognition"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "attention strategies for multi source sequence to sequence learning"
    - "lipnet end to end sentence level lipreading"
    - "lip reading sentences in the wild"
    - "using the output embedding to improve language models"

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.CL(Computation and Language)