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: "a dataset and exploration of models for understanding video data through fill in the blank question answering"
  Abstract: While deep convolutional neural networks frequently approach or exceed human-level performance at benchmark tasks involving static images, extending this success to moving images is not straightforward. Having models which can learn to understand video is of interest for many applications, including content recommendation, prediction, summarization, event/object detection and understanding human visual perception, but many domains lack sufficient data to explore and perfect video models. In order to address the need for a simple, quantitative benchmark for developing and understanding video, we present MovieFIB, a fill-in-the-blank question-answering dataset with over 300,000 examples, based on descriptive video annotations for the visually impaired. In addition to presenting statistics and a description of the dataset, we perform a detailed analysis of 5 different models' predictions, and compare these with human performance. We investigate the relative importance of language, static (2D) visual features, and moving (3D) visual features; the effects of increasing dataset size, the number of frames sampled; and of vocabulary size. We illustrate that: this task is not solvable by a language model alone; our model combining 2D and 3D visual information indeed provides the best result; all models perform significantly worse than human-level. We provide human evaluations for responses given by different models and find that accuracy on the MovieFIB evaluation corresponds well with human judgement. We suggest avenues for improving video models, and hope that the proposed dataset can be useful for measuring and encouraging progress in this very interesting field.

Connected through hyperedge HE-1 (induced by source paper "tvqa spatio temporal grounding for video question answering"):
  with members:
    - "neural baby talk"
    - "know what you don t know unanswerable questions for squad"
    - "mattnet modular attention network for referring expression comprehension"
    - "grounded video description"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tvqa localized compositional video question answering"
    - "bsn boundary sensitive network for temporal action proposal generation"

Connected through hyperedge HE-2 (induced by source paper "audio visual scene aware dialog"):
  with members:
    - "tvqa localized compositional video question answering"
    - "the activitynet large scale activity recognition challenge 2018 summary"
    - "a dataset for document grounded conversations"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "dense captioning events in videos"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-3 (induced by source paper "are we asking the right questions in movieqa"):
  with members:
    - "comparing attention based convolutional and recurrent neural networks success and limitations in machine reading comprehension"
    - "speaker naming in movies"
    - "multimodal dual attention memory for video story question answering"
    - "from trailers to storylines an efficient way to learn from movies"
    - "tvqa localized compositional video question answering"
    - "movie question answering remembering the textual cues for layered visual contents"
    - "generating descriptions with grounded and co referenced people"
    - "a read write memory network for movie story understanding"

Connected through hyperedge HE-4 (induced by source paper "tutorialvqa question answering dataset for tutorial videos"):
  with members:
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "learning recurrent span representations for extractive question answering"
    - "tgif qa toward spatio temporal reasoning in visual question answering"
    - "deepstory video story qa by deep embedded memory networks"
    - "squad 100 000 questions for machine comprehension of text"
    - "leveraging video descriptions to learn video question answering"
    - "vqa visual question answering"
    - "what s cookin interpreting cooking videos using text speech and vision"

Connected through hyperedge HE-5 (induced by source paper "visual text correction"):
  with members:
    - "video fill in the blank using lr rl lstms with spatial temporal attentions"
    - "convolutional sequence to sequence learning"
    - "language modeling with gated convolutional networks"
    - "end to end concept word detection for video captioning retrieval and question answering"
    - "dynamic memory networks for visual and textual question answering"
    - "exploring models and data for image question answering"
    - "vqa visual question answering"
    - "a dataset for movie description"

Connected through hyperedge HE-6 (induced by source paper "a joint sequence fusion model for video question answering and retrieval"):
  with members:
    - "video fill in the blank using lr rl lstms with spatial temporal attentions"
    - "learning from video and text via large scale discriminative clustering"
    - "tgif qa toward spatio temporal reasoning in visual question answering"
    - "hadamard product for low rank bilinear pooling"
    - "learning language visual embedding for movie understanding with natural language"
    - "cnn architectures for large scale audio classification"
    - "end to end concept word detection for video captioning retrieval and question answering"
    - "video fill in the blank with merging lstms"

Connected through hyperedge HE-7 (induced by source paper "video fill in the blank using lr rl lstms with spatial temporal attentions"):
  with members:
    - "end to end concept word detection for video captioning retrieval and question answering"
    - "dynamic memory networks for visual and textual question answering"
    - "exploring models and data for image question answering"
    - "vqa visual question answering"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "a dataset for movie description"
    - "show attend and tell neural image caption generation with visual attention"
    - "densecap fully convolutional localization networks for dense captioning"

Connected through hyperedge HE-8 (induced by source paper "learning fashion compatibility with bidirectional lstms"):
  with members:
    - "automatic spatially aware fashion concept discovery"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"
    - "video fill in the blank with merging lstms"
    - "modeling visual compatibility through hierarchical mid level elements"
    - "jointly modeling embedding and translation to bridge video and language"
    - "learning visual clothing style with heterogeneous dyadic co occurrences"
    - "uncovering temporal context for video question and answering"
    - "image based recommendations on styles and substitutes"

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.CV(Computer Vision and Pattern Recognition)