human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "open ended long form video question answering via hierarchical convolutional self attention networks"):
  Abstract: Open-ended video question answering aims to automatically generate the natural-language answer from referenced video contents according to the given question. Currently, most existing approaches focus on short-form video question answering with multi-modal recurrent encoder-decoder networks. Although these works have achieved promising performance, they may still be ineffectively applied to long-form video question answering due to the lack of long-range dependency modeling and the suffering from the heavy computational cost. To tackle these problems, we propose a fast Hierarchical Convolutional Self-Attention encoder-decoder network(HCSA). Concretely, we first develop a hierarchical convolutional self-attention encoder to efficiently model long-form video contents, which builds the hierarchical structure for video sequences and captures question-aware long-range dependencies from video context. We then devise a multi-scale attentive decoder to incorporate multi-layer video representations for answer generation, which avoids the information missing of the top encoder layer. The extensive experiments show the effectiveness and efficiency of our method.

Includes members:
  Member "motion appearance co memory networks for video question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "bottom up and top down attention for image captioning and visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "convolutional sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "video fill in the blank with merging lstms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a better way to attend attention with trees for video question answering")
  Member "hierarchical question image co attention for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "leveraging video descriptions to learn video question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "visual question answering on 360 deg images")
  Member "visual question answering a survey of methods and datasets":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "evaluating the progress of deep learning for visual relational concepts")

Question: Please tell me which class the source paper 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)