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: "video fill in the blank with merging lstms"
  Abstract: Given a video and its incomplete textural description with missing words, the Video-Fill-in-the-Blank (ViFitB) task is to automatically find the missing word. The contextual information of the sentences are important to infer the missing words; the visual cues are even more crucial to get a more accurate inference. In this paper, we presents a new method which intuitively takes advantage of the structure of the sentences and employs merging LSTMs (to merge two LSTMs) to tackle the problem with embedded textural and visual cues. In the experiments, we have demonstrated the superior performance of the proposed method on the challenging "Movie Fill-in-the-Blank" dataset.

Connected through hyperedge HE-1 (induced by source paper "open ended long form video question answering via hierarchical convolutional self attention networks"):
  with members:
    - "motion appearance co memory networks for video question answering"
    - "attention is all you need"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "convolutional sequence to sequence learning"
    - "hierarchical question image co attention for visual question answering"
    - "leveraging video descriptions to learn video question answering"
    - "visual question answering a survey of methods and datasets"
    - "movieqa understanding stories in movies through question answering"

Connected through hyperedge HE-2 (induced by source paper "a better way to attend attention with trees for video question answering"):
  with members:
    - "tree structured reinforcement learning for sequential object localization"
    - "tree to sequence attentional neural machine translation"
    - "enhancing and combining sequential and tree lstm for natural language inference"
    - "image captioning with semantic attention"
    - "a focused dynamic attention model for visual question answering"
    - "enhanced lstm for natural language inference"
    - "hierarchical question image co attention for visual question answering"
    - "tgif a new dataset and benchmark on animated gif description"

Connected through hyperedge HE-3 (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"
    - "a dataset and exploration of models for understanding video data through fill in the blank question answering"
    - "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"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "the forgettable watcher model for video question answering"):
  with members:
    - "tgif a new dataset and benchmark on animated gif description"
    - "visual7w grounded question answering in images"
    - "exploring models and data for image question answering"
    - "uncovering temporal context for video question and 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"

Connected through hyperedge HE-6 (induced by source paper "a dataset and exploration of models for understanding video data through fill in the blank question answering"):
  with members:
    - "end to end concept word detection for video captioning retrieval and question answering"
    - "recurrent batch normalization"
    - "exploring models and data for image question answering"
    - "uncovering temporal context for video question and answering"
    - "unsupervised learning of video representations using lstms"
    - "delving deeper into convolutional networks for learning video representations"
    - "vqa visual question answering"
    - "visual madlibs fill in the blank image generation and question answering"

Connected through hyperedge HE-7 (induced by source paper "end to end concept word detection for video captioning retrieval and question answering"):
  with members:
    - "image captioning with semantic attention"
    - "learning language visual embedding for movie understanding with natural language"
    - "image captioning and visual question answering based on attributes and external knowledge"
    - "temporal tessellation a unified approach for video analysis"
    - "captioning images with diverse objects"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "what value do explicit high level concepts have in vision to language problems"
    - "the long short story of movie description"

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)