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: "movie question answering remembering the textual cues for layered visual contents"
  Abstract: Movies provide us with a mass of visual content as well as attracting stories. Existing methods have illustrated that understanding movie stories through only visual content is still a hard problem. In this paper, for answering questions about movies, we put forward a Layered Memory Network (LMN) that represents frame-level and clip-level movie content by the Static Word Memory module and the Dynamic Subtitle Memory module, respectively. Particularly, we firstly extract words and sentences from the training movie subtitles. Then the hierarchically formed movie representations, which are learned from LMN, not only encode the correspondence between words and visual content inside frames, but also encode the temporal alignment between sentences and frames inside movie clips. We also extend our LMN model into three variant frameworks to illustrate the good extendable capabilities. We conduct extensive experiments on the MovieQA dataset. With only visual content as inputs, LMN with frame-level representation obtains a large performance improvement. When incorporating subtitles into LMN to form the clip-level representation, we achieve the state-of-the-art performance on the online evaluation task of 'Video+Subtitles'. The good performance successfully demonstrates that the proposed framework of LMN is effective and the hierarchically formed movie representations have good potential for the applications of movie question answering.

Connected through hyperedge HE-1 (induced by source paper "hierarchical conditional relation networks for video question answering"):
  with members:
    - "learning to reason with relational video representation for question answering"
    - "progressive attention memory network for movie story question answering"
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "long term feature banks for detailed video understanding"
    - "bilinear attention networks"
    - "non local netvlad encoding for video classification"
    - "hierarchical video frame sequence representation with deep convolutional graph network"
    - "motion appearance co memory networks for video question answering"

Connected through hyperedge HE-2 (induced by source paper "learning to reason with relational video representation for question answering"):
  with members:
    - "learning regularity in skeleton trajectories for anomaly detection in videos"
    - "progressive attention memory network for movie story question answering"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "motion appearance co memory networks for video question answering"
    - "multimodal dual attention memory for video story question answering"
    - "compositional attention networks for machine reasoning"
    - "variational memory encoder decoder"
    - "tvqa localized compositional video question answering"

Connected through hyperedge HE-3 (induced by source paper "gaining extra supervision via multi task learning for multi modal video question answering"):
  with members:
    - "a joint sequence fusion model for video question answering and retrieval"
    - "multimodal dual attention memory for video story question answering"
    - "tvqa localized compositional video question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "visual question generation as dual task of visual question answering"
    - "attention is all you need"
    - "obj2text generating visually descriptive language from object layouts"
    - "a read write memory network for movie story understanding"

Connected through hyperedge HE-4 (induced by source paper "episodic memory reader learning what to remember for question answering from streaming data"):
  with members:
    - "multimodal dual attention memory for video story question answering"
    - "deep sets"
    - "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"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "progressive attention memory network for movie story question answering"):
  with members:
    - "motion appearance co memory networks for video question answering"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "multimodal dual attention memory for video story question answering"
    - "tvqa localized compositional video question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "attention is all you need"
    - "tgif qa toward spatio temporal reasoning in visual question answering"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-6 (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"
    - "generating descriptions with grounded and co referenced people"
    - "a read write memory network for movie story understanding"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-7 (induced by source paper "adversarial multimodal network for movie question answering"):
  with members:
    - "attention is all you need"
    - "a read write memory network for movie story understanding"
    - "survey of recent advances in visual question answering"
    - "deepstory video story qa by deep embedded memory networks"
    - "learning to compose neural networks for question answering"
    - "visual question answering a survey of methods and datasets"
    - "marioqa answering questions by watching gameplay videos"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"

Connected through hyperedge HE-8 (induced by source paper "holistic multi modal memory network for movie question answering"):
  with members:
    - "compositional attention networks for machine reasoning"
    - "neural generation of diverse questions using answer focus contextual and linguistic features"
    - "attention on attention architectures for visual question answering vqa"
    - "tvqa localized compositional video question answering"
    - "decoupled novel object captioner"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "learning two branch neural networks for image text matching tasks"
    - "attention is all you need"

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)