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 "figurenet a deep learning model for question answering on scientific plots"):
  Abstract: Deep Learning has managed to push boundaries in a wide variety of tasks. One area of interest is to tackle problems in reasoning and understanding, with an aim to emulate human intelligence. In this work, we describe a deep learning model that addresses the reasoning task of question-answering on categorical plots. We introduce a novel architecture FigureNet, that learns to identify various plot elements, quantify the represented values and determine a relative ordering of these statistical values. We test our model on the FigureQA dataset which provides images and accompanying questions for scientific plots like bar graphs and pie charts, augmented with rich annotations. Our approach outperforms the state-of-the-art Relation Networks baseline by approximately $7\%$ on this dataset, with a training time that is over an order of magnitude lesser.

Includes members:
  Member "mutan multimodal tucker fusion for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "component analysis for visual question answering architectures")
  Member "a simple neural network module for relational reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "learning to reason end to end module networks for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gradient based adversarial attacks on categorical sequence models via traversing an embedded world")
  Member "figureqa an annotated figure dataset for visual reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "plotqa reasoning over scientific plots")
  Member "film visual reasoning with a general conditioning layer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "in defense of grid features for visual question answering")
  Member "dual attention networks for multimodal reasoning and matching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "vsgnet spatial attention network for detecting human object interactions using graph convolutions")
  Member "ask your neurons a deep learning approach to visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "visual grounding in video for unsupervised word translation")
  Member "clevr a diagnostic dataset for compositional language and elementary visual reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")

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)