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: "from film to video multi turn question answering with multi modal context"
  Abstract: Understanding audio-visual content and the ability to have an informative conversation about it have both been challenging areas for intelligent systems. The Audio Visual Scene-aware Dialog (AVSD) challenge, organized as a track of the Dialog System Technology Challenge 7 (DSTC7), proposes a combined task, where a system has to answer questions pertaining to a video given a dialogue with previous question-answer pairs and the video itself. We propose for this task a hierarchical encoder-decoder model which computes a multi-modal embedding of the dialogue context. It first embeds the dialogue history using two LSTMs. We extract video and audio frames at regular intervals and compute semantic features using pre-trained I3D and VGGish models, respectively. Before summarizing both modalities into fixed-length vectors using LSTMs, we use FiLM blocks to condition them on the embeddings of the current question, which allows us to reduce the dimensionality considerably. Finally, we use an LSTM decoder that we train with scheduled sampling and evaluate using beam search. Compared to the modality-fusing baseline model released by the AVSD challenge organizers, our model achieves a relative improvements of more than 16%, scoring 0.36 BLEU-4 and more than 33%, scoring 0.997 CIDEr.

Connected through hyperedge HE-1 (induced by source paper "videonavqa bridging the gap between visual and embodied question answering"):
  with members:
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "multi target embodied question answering"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "habitat a platform for embodied ai research"
    - "murel multimodal relational reasoning for visual question answering"
    - "learning visual question answering by bootstrapping hard attention"
    - "systematic generalization what is required and can it be learned"

Connected through hyperedge HE-2 (induced by source paper "video question generation via cross modal self attention networks learning"):
  with members:
    - "lifelong learning for image captioning by asking natural language questions"
    - "end to end dense video captioning with masked transformer"
    - "tvqa localized compositional video question answering"
    - "learning to ask neural question generation for reading comprehension"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "visual question generation as dual task of visual question answering"
    - "attention is all you need"
    - "obj2text generating visually descriptive language from object layouts"

Connected through hyperedge HE-3 (induced by source paper "tct a cross supervised learning method for multimodal sequence representation"):
  with members:
    - "multimodal transformer networks for end to end video grounded dialogue systems"
    - "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"
    - "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)