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: "tvqa localized compositional video question answering"
  Abstract: Recent years have witnessed an increasing interest in image-based question-answering (QA) tasks. However, due to data limitations, there has been much less work on video-based QA. In this paper, we present TVQA, a large-scale video QA dataset based on 6 popular TV shows. TVQA consists of 152,545 QA pairs from 21,793 clips, spanning over 460 hours of video. Questions are designed to be compositional in nature, requiring systems to jointly localize relevant moments within a clip, comprehend subtitle-based dialogue, and recognize relevant visual concepts. We provide analyses of this new dataset as well as several baselines and a multi-stream end-to-end trainable neural network framework for the TVQA task. The dataset is publicly available at this http URL

Connected through hyperedge HE-1 (induced by source paper "hierarchical conditional relation networks for video question answering"):
  with members:
    - "learning to reason with relational video representation for question answering"
    - "progressive attention memory network for movie story question answering"
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "long term feature banks for detailed video understanding"
    - "bilinear attention networks"
    - "non local netvlad encoding for video classification"
    - "hierarchical video frame sequence representation with deep convolutional graph network"
    - "motion appearance co memory networks for video question answering"

Connected through hyperedge HE-2 (induced by source paper "manymodalqa modality disambiguation and qa over diverse inputs"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "recipeqa a challenge dataset for multimodal comprehension of cooking recipes"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "coqa a conversational question answering challenge"
    - "universal sentence encoder"
    - "adversarial tableqa attention supervision for question answering on tables"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "tvr a large scale dataset for video subtitle moment retrieval"):
  with members:
    - "tvqa spatio temporal grounding for video question answering"
    - "vatex a large scale high quality multilingual dataset for video and language research"
    - "temporal localization of moments in video collections with natural language"
    - "roberta a robustly optimized bert pretraining approach"
    - "excl extractive clip localization using natural language descriptions"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "mattnet modular attention network for referring expression comprehension"

Connected through hyperedge HE-4 (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"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "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-5 (induced by source paper "cut based graph learning networks to discover compositional structure of sequential video data"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "hierarchical video frame sequence representation with deep convolutional graph network"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "attention is all you need"
    - "neural message passing for quantum chemistry"
    - "vain attentional multi agent predictive modeling"
    - "convolutional sequence to sequence learning"
    - "one shot imitation learning"

Connected through hyperedge HE-6 (induced by source paper "video2commonsense generating commonsense descriptions to enrich video captioning"):
  with members:
    - "vqa lol visual question answering under the lens of logic"
    - "procedure planning in instructional videos"
    - "lxmert learning cross modality encoder representations from transformers"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "blocksworld revisited learning and reasoning to generate event sequences from image pairs"
    - "roberta a robustly optimized bert pretraining approach"
    - "from recognition to cognition visual commonsense reasoning"

Connected through hyperedge HE-7 (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"
    - "multimodal transformer networks for end to end video grounded dialogue systems"
    - "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"
    - "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-8 (induced by source paper "investigating the decoders of maximum likelihood sequence models a look ahead approach"):
  with members:
    - "on nmt search errors and model errors cat got your tongue"
    - "end to end dense video captioning with masked transformer"
    - "a call for clarity in reporting bleu scores"
    - "optimal completion distillation for sequence learning"
    - "attention is all you need"
    - "adversarial learning for neural dialogue generation"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "multi30k multilingual english german image descriptions"

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)