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 "mtle a multitask learning encoder of visual feature representations for video and movie description"):
  Abstract: Learning visual feature representations for video analysis is a daunting task that requires a large amount of training samples and a proper generalization framework. Many of the current state of the art methods for video captioning and movie description rely on simple encoding mechanisms through recurrent neural networks to encode temporal visual information extracted from video data. In this paper, we introduce a novel multitask encoder-decoder framework for automatic semantic description and captioning of video sequences. In contrast to current approaches, our method relies on distinct decoders that train a visual encoder in a multitask fashion. Our system does not depend solely on multiple labels and allows for a lack of training data working even with datasets where only one single annotation is viable per video. Our method shows improved performance over current state of the art methods in several metrics on multi-caption and single-caption datasets. To the best of our knowledge, our method is the first method to use a multitask approach for encoding video features. Our method demonstrates its robustness on the Large Scale Movie Description Challenge (LSMDC) 2017 where our method won the movie description task and its results were ranked among other competitors as the most helpful for the visually impaired.

Includes members:
  Member "consistent multitask learning with nonlinear output relations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "leveraging low rank relations between surrogate tasks in structured prediction")
  Member "multi task video captioning with video and entailment generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "delving deeper into the decoder for video captioning")
  Member "video captioning with transferred semantic attributes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi modal dense video captioning")
  Member "semantic compositional networks for visual captioning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "improving lstm based video description with linguistic knowledge mined from text":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "augmenting neural machine translation with knowledge graphs")
  Member "vqa visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "the long short story of movie description":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "skip thought vectors":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on contextual embeddings")

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.LG(Machine Learning)