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: "end to end audio visual scene aware dialog using multimodal attention based video features"
  Abstract: Dialog systems need to understand dynamic visual scenes in order to have conversations with users about the objects and events around them. Scene-aware dialog systems for real-world applications could be developed by integrating state-of-the-art technologies from multiple research areas, including: end-to-end dialog technologies, which generate system responses using models trained from dialog data; visual question answering (VQA) technologies, which answer questions about images using learned image features; and video description technologies, in which descriptions/captions are generated from videos using multimodal information. We introduce a new dataset of dialogs about videos of human behaviors. Each dialog is a typed conversation that consists of a sequence of 10 question-and-answer(QA) pairs between two Amazon Mechanical Turk (AMT) workers. In total, we collected dialogs on roughly 9,000 videos. Using this new dataset for Audio Visual Scene-aware dialog (AVSD), we trained an end-to-end conversation model that generates responses in a dialog about a video. Our experiments demonstrate that using multimodal features that were developed for multimodal attention-based video description enhances the quality of generated dialog about dynamic scenes (videos). Our dataset, model code and pretrained models will be publicly available for a new Video Scene-Aware Dialog challenge.

Connected through hyperedge HE-1 (induced by source paper "multi step joint modality attention network for scene aware dialogue system"):
  with members:
    - "dual attention networks for visual reference resolution in visual dialog"
    - "multi step reasoning via recurrent dual attention for visual dialog"
    - "bilinear attention networks"
    - "tvqa localized compositional video question answering"
    - "recursive visual attention in visual dialog"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "video question answering via attribute augmented attention network learning"
    - "visual reference resolution using attention memory for visual dialog"

Connected through hyperedge HE-2 (induced by source paper "multi layer content interaction through quaternion product for visual question answering"):
  with members:
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "deep modular co attention networks for visual question answering"
    - "bilinear attention networks"
    - "quaternet a quaternion based recurrent model for human motion"
    - "quaternion convolutional neural networks for end to end automatic speech recognition"
    - "learning to count objects in natural images for visual question answering"
    - "deep contextualized word representations"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "factor graph attention"):
  with members:
    - "a simple baseline for audio visual scene aware dialog"
    - "diverse and coherent paragraph generation from images"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "audio visual scene aware dialog avsd challenge at dstc7"
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "structured attention networks"

Connected through hyperedge HE-4 (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"
    - "entropy enhanced multimodal attention model for scene aware dialogue generation"
    - "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"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "tvqa localized compositional video question answering"

Connected through hyperedge HE-5 (induced by source paper "multi modal attention network learning for semantic source code retrieval"):
  with members:
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "multimodal dual attention memory for video story question answering"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "multimodal machine learning a survey and taxonomy"
    - "attention is all you need"
    - "a deep reinforced model for abstractive summarization"
    - "dual attention networks for multimodal reasoning and matching"
    - "image captioning with semantic attention"

Connected through hyperedge HE-6 (induced by source paper "multimodal transformer networks for end to end video grounded dialogue systems"):
  with members:
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "generating wikipedia by summarizing long sequences"
    - "training millions of personalized dialogue agents"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "jointly localizing and describing events for dense video captioning"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "the eighth dialog system technology challenge"):
  with members:
    - "hyst a hybrid approach for flexible and accurate dialogue state tracking"
    - "bert dst scalable end to end dialogue state tracking with bidirectional encoder representations from transformer"
    - "dialog system technology challenge 7"
    - "towards scalable multi domain conversational agents the schema guided dialogue dataset"
    - "convlab multi domain end to end dialog system platform"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-8 (induced by source paper "multimodal embeddings from language models"):
  with members:
    - "multimodal abstractive summarization for how2 videos"
    - "behavior gated language models"
    - "multi task learning for multi modal emotion recognition and sentiment analysis"
    - "m bert injecting multimodal information in the bert structure"
    - "multimodal language analysis with recurrent multistage fusion"
    - "convolutional attention networks for multimodal emotion recognition from speech and text data"
    - "multimodal speech emotion recognition using audio and text"
    - "frage frequency agnostic word representation"

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)