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: "multimodal transformer networks for end to end video grounded dialogue systems"
  Abstract: Developing Video-Grounded Dialogue Systems (VGDS), where a dialogue is conducted based on visual and audio aspects of a given video, is significantly more challenging than traditional image or text-grounded dialogue systems because (1) feature space of videos span across multiple picture frames, making it difficult to obtain semantic information; and (2) a dialogue agent must perceive and process information from different modalities (audio, video, caption, etc.) to obtain a comprehensive understanding. Most existing work is based on RNNs and sequence-to-sequence architectures, which are not very effective for capturing complex long-term dependencies (like in videos). To overcome this, we propose Multimodal Transformer Networks (MTN) to encode videos and incorporate information from different modalities. We also propose query-aware attention through an auto-encoder to extract query-aware features from non-text modalities. We develop a training procedure to simulate token-level decoding to improve the quality of generated responses during inference. We get state of the art performance on Dialogue System Technology Challenge 7 (DSTC7). Our model also generalizes to another multimodal visual-grounded dialogue task, and obtains promising performance. We implemented our models using PyTorch and the code is released at this https URL.

Connected through hyperedge HE-1 (induced by source paper "multimodal transformer with pointer network for the dstc8 avsd challenge"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "progressive attention memory network for movie story question answering"
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "motion appearance co memory networks for video question answering"
    - "tvqa localized compositional video question answering"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attention is all you need"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"

Connected through hyperedge HE-2 (induced by source paper "bridging text and video a universal multimodal transformer for video audio scene aware dialog"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "incremental transformer with deliberation decoder for document grounded conversations"
    - "the curious case of neural text degeneration"
    - "videobert a joint model for video and language representation learning"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"
    - "cider consensus based image description evaluation"

Connected through hyperedge HE-3 (induced by source paper "tct a cross supervised learning method for multimodal sequence representation"):
  with members:
    - "reactive multi stage feature fusion for multimodal dialogue modeling"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "how2 a large scale dataset for multimodal language understanding"
    - "from film to video multi turn question answering with multi modal context"
    - "multimodal machine learning a survey and taxonomy"
    - "attention is all you need"
    - "dense captioning events in videos"
    - "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.CL(Computation and Language)