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 "recurrent memory addressing for describing videos"):
  Abstract: Deep Neural Network architectures with external memory components allow the model to perform inference and capture long term dependencies, by storing information explicitly. In this paper, we generalize Key-Value Memory Networks to a multimodal setting, introducing a novel key-addressing mechanism to deal with sequence-to-sequence models. The advantages of the framework are demonstrated on the task of video captioning, i.e generating natural language descriptions for videos. Conditioning on the previous time-step attention distributions for the key-value memory slots, we introduce a temporal structure in the memory addressing schema. The proposed model naturally decomposes the problem of video captioning into vision and language segments, dealing with them as key-value pairs. More specifically, we learn a semantic embedding (v) corresponding to each frame (k) in the video, thereby creating (k, v) memory slots. This allows us to exploit the temporal dependencies at multiple hierarchies (in the recurrent key-addressing; and in the language decoder). Exploiting this flexibility of the framework, we additionally capture spatial dependencies while mapping from the visual to semantic embedding. Extensive experiments on the Youtube2Text dataset demonstrate usefulness of recurrent key-addressing, while achieving competitive scores on BLEU@4, METEOR metrics against state-of-the-art models.

Includes members:
  Member "image captioning with semantic attention":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "key value memory networks for directly reading documents":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attention over parameters for dialogue systems")
  Member "encode review and decode reviewer module for caption generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mrrc multiple role representation crossover interpretation for image captioning with r cnn feature distribution composition fdc")
  Member "jointly modeling embedding and translation to bridge video and language":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "end to end memory networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning video object segmentation from unlabeled videos")
  Member "building end to end dialogue systems using generative hierarchical neural network models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attention over parameters for dialogue systems")
  Member "delving deeper into convolutional networks for learning video representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "vqa visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")

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)