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 "are we asking the right questions in movieqa"):
  Abstract: Joint vision and language tasks like visual question answering are fascinating because they explore high-level understanding, but at the same time, can be more prone to language biases. In this paper, we explore the biases in the MovieQA dataset and propose a strikingly simple model which can exploit them. We find that using the right word embedding is of utmost importance. By using an appropriately trained word embedding, about half the Question-Answers (QAs) can be answered by looking at the questions and answers alone, completely ignoring narrative context from video clips, subtitles, and movie scripts. Compared to the best published papers on the leaderboard, our simple question + answer only model improves accuracy by 5% for video + subtitle category, 5% for subtitle, 15% for DVS and 6% higher for scripts.

Includes members:
  Member "comparing attention based convolutional and recurrent neural networks success and limitations in machine reading comprehension":
    also connected through other hyperedges:
      (none)
  Member "speaker naming in movies":
    also connected through other hyperedges:
      (none)
  Member "multimodal dual attention memory for video story question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hierarchical conditional relation networks for video question answering")
  Member "from trailers to storylines an efficient way to learn from movies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a graph based framework to bridge movies and synopses")
  Member "tvqa localized compositional video question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical conditional relation networks for video question answering")
  Member "movie question answering remembering the textual cues for layered visual contents":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hierarchical conditional relation networks for video question answering")
  Member "generating descriptions with grounded and co referenced people":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "visual grounding in video for unsupervised word translation")
  Member "a read write memory network for movie story understanding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation")

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)