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: "uncovering temporal context for video question and answering"
  Abstract: In this work, we introduce Video Question Answering in temporal domain to infer the past, describe the present and predict the future. We present an encoder-decoder approach using Recurrent Neural Networks to learn temporal structures of videos and introduce a dual-channel ranking loss to answer multiple-choice questions. We explore approaches for finer understanding of video content using question form of "fill-in-the-blank", and managed to collect 109,895 video clips with duration over 1,000 hours from TACoS, MPII-MD, MEDTest 14 datasets, while the corresponding 390,744 questions are generated from annotations. Extensive experiments demonstrate that our approach significantly outperforms the compared baselines.

Connected through hyperedge HE-1 (induced by source paper "focal visual text attention for memex question answering"):
  with members:
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "differential attention for visual question answering"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "ivqa inverse visual question answering"
    - "an analysis of visual question answering algorithms"

Connected through hyperedge HE-2 (induced by source paper "assessing the robustness of visual question answering"):
  with members:
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "robustness via curvature regularization and vice versa"
    - "visual question answering datasets algorithms and future challenges"
    - "mutan multimodal tucker fusion for visual question answering"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "an analysis of visual question answering algorithms"
    - "hadamard product for low rank bilinear pooling"
    - "ask your neurons a deep learning approach to visual question answering"

Connected through hyperedge HE-3 (induced by source paper "how to make a blt sandwich learning to reason towards understanding web instructional videos"):
  with members:
    - "end to end dense video captioning with masked transformer"
    - "compositional attention networks for machine reasoning"
    - "videos as space time region graphs"
    - "unsupervised visual linguistic reference resolution in instructional videos"
    - "weakly supervised visual grounding of phrases with linguistic structures"
    - "towards automatic learning of procedures from web instructional videos"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "leveraging video descriptions to learn video question answering"

Connected through hyperedge HE-4 (induced by source paper "visual reference resolution using attention memory for visual dialog"):
  with members:
    - "unsupervised visual linguistic reference resolution in instructional videos"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "improving coreference resolution by learning entity level distributed representations"
    - "training recurrent answering units with joint loss minimization for vqa"
    - "text guided attention model for image captioning"
    - "hierarchical attention networks"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-5 (induced by source paper "multi modal factorized bilinear pooling with co attention learning for visual question answering"):
  with members:
    - "hadamard product for low rank bilinear pooling"
    - "training recurrent answering units with joint loss minimization for vqa"
    - "dual attention networks for multimodal reasoning and matching"
    - "a focused dynamic attention model for visual question answering"
    - "dualnet domain invariant network for visual question answering"
    - "multimodal residual learning for visual qa"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "learning to compose neural networks for question answering"

Connected through hyperedge HE-6 (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"
    - "a dataset and exploration of models for understanding video data through fill in the blank question answering"
    - "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"
    - "image based recommendations on styles and substitutes"

Connected through hyperedge HE-7 (induced by source paper "encoding video and label priors for multi label video classification on youtube 8m dataset"):
  with members:
    - "face r cnn"
    - "cnn architectures for large scale audio classification"
    - "end to end concept word detection for video captioning retrieval and question answering"
    - "youtube 8m a large scale video classification benchmark"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "end to end memory networks"
    - "movieqa understanding stories in movies through question answering"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-8 (induced by source paper "the forgettable watcher model for video question answering"):
  with members:
    - "video fill in the blank with merging lstms"
    - "tgif a new dataset and benchmark on animated gif description"
    - "visual7w grounded question answering in images"
    - "exploring models and data for image question answering"
    - "image question answering using convolutional neural network with dynamic parameter prediction"
    - "learning to answer questions from image using convolutional neural network"
    - "stacked attention networks for image question answering"
    - "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.CV(Computer Vision and Pattern Recognition)