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 "learning to caption images with two stream attention and sentence auto encoder"):
  Abstract: Automatically generating natural language descriptions from an image is a challenging problem in artificial intelligence that requires a good understanding of the correlations between visual and textual cues. To bridge these two modalities, state-of-the-art methods commonly use a dynamic interface between image and text, called attention, that learns to identify related image parts to estimate the next word conditioned on the previous steps. While this mechanism is effective, it fails to find the right associations between visual and textual cues when they are noisy. In this paper we propose two novel approaches to address this issue - (i) a two-stream attention mechanism that can automatically discover latent categories and relate them to image regions based on the previously generated words, (ii) a regularization technique that encapsulates the syntactic and semantic structure of captions and improves the optimization of the image captioning model. Our qualitative and quantitative results demonstrate remarkable improvements on the MSCOCO dataset setting and lead to new state-of-the-art performances for image captioning.

Includes members:
  Member "improving image captioning by leveraging knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an ontology aware framework for audio event classification")
  Member "attention on attention for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deconfounded image captioning a causal retrospect")
  Member "regularizing rnns for caption generation by reconstructing the past with the present":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "language features matter effective language representations for vision language tasks")
  Member "auto encoding scene graphs for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "bottom up and top down attention for image captioning and visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "convolutional sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "sequence to sequence learning as beam search optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gradient based adversarial attacks on categorical sequence models via traversing an embedded world")
  Member "knowing when to look adaptive attention via a visual sentinel for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relationship embedded representation learning for grounding referring expressions")

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.CL(Computation and Language)