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 "generating diverse and informative natural language fashion feedback"):
  Abstract: Recent advances in multi-modal vision and language tasks enable a new set of applications. In this paper, we consider the task of generating natural language fashion feedback on outfit images. We collect a unique dataset, which contains outfit images and corresponding positive and constructive fashion feedback. We treat each feedback type separately, and train deep generative encoder-decoder models with visual attention, similar to the standard image captioning pipeline. Following this approach, the generated sentences tend to be too general and non-informative. We propose an alternative decoding technique based on the Maximum Mutual Information objective function, which leads to more diverse and detailed responses. We evaluate our model with common language metrics, and also show human evaluation results. This technology is applied within the ``Alexa, how do I look?'' feature, publicly available in Echo Look devices.

Includes members:
  Member "netizen style commenting on fashion photos dataset and diversity measures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fashion retrieval via graph reasoning networks on a similarity pyramid")
  Member "improved image captioning via policy gradient optimization of spider":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "speaking the same language matching machine to human captions by adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "say as you wish fine grained control of image caption generation with abstract scene graphs")
  Member "bottom up and top down attention for image captioning and visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "towards diverse and natural image descriptions via a conditional gan":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "ssd single shot multibox detector":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "knowing when to look adaptive attention via a visual sentinel for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "self critical sequence training for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")

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)