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: "entropy enhanced multimodal attention model for scene aware dialogue generation"
  Abstract: With increasing information from social media, there are more and more videos available. Therefore, the ability to reason on a video is important and deserves to be discussed. TheDialog System Technology Challenge (DSTC7) (Yoshino et al. 2018) proposed an Audio Visual Scene-aware Dialog (AVSD) task, which contains five modalities including video, dialogue history, summary, and caption, as a scene-aware environment. In this paper, we propose the entropy-enhanced dynamic memory network (DMN) to effectively model video modality. The attention-based GRU in the proposed model can improve the model's ability to comprehend and memorize sequential information. The entropy mechanism can control the attention distribution higher, so each to-be-answered question can focus more specifically on a small set of video segments. After the entropy-enhanced DMN secures the video context, we apply an attention model that in-corporates summary and caption to generate an accurate answer given the question about the video. In the official evaluation, our system can achieve improved performance against the released baseline model for both subjective and objective evaluation metrics.

Connected through hyperedge HE-1 (induced by source paper "learning question guided video representation for multi turn video question answering"):
  with members:
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "reactive multi stage feature fusion for multimodal dialogue modeling"
    - "audio visual scene aware dialog"
    - "motion appearance co memory networks for video question answering"
    - "context attention and audio feature explorations for audio visual scene aware dialog"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "tvqa localized compositional video question answering"

Connected through hyperedge HE-2 (induced by source paper "hscjn a holistic semantic constraint joint network for diverse response generation"):
  with members:
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "dp gan diversity promoting generative adversarial network for generating informative and diversified text"
    - "a knowledge grounded neural conversation model"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "vocabulary selection strategies for neural machine translation"
    - "neural machine translation with word predictions"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a simple fast diverse decoding algorithm for neural generation"

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)