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 "improving reinforcement learning based image captioning with natural language prior"):
  Abstract: Recently, Reinforcement Learning (RL) approaches have demonstrated advanced performance in image captioning by directly optimizing the metric used for testing. However, this shaped reward introduces learning biases, which reduces the readability of generated text. In addition, the large sample space makes training unstable and slow. To alleviate these issues, we propose a simple coherent solution that constrains the action space using an n-gram language prior. Quantitative and qualitative evaluations on benchmarks show that RL with the simple add-on module performs favorably against its counterpart in terms of both readability and speed of convergence. Human evaluation results show that our model is more human readable and graceful. The implementation will become publicly available upon the acceptance of the paper.

Includes members:
  Member "deep reinforcement learning based image captioning with embedding reward":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "say as you wish fine grained control of image caption generation with abstract scene graphs")
  Member "scene graph generation from objects phrases and region captions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "towards diverse and natural image descriptions via a conditional gan":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "a deep reinforced model for abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "time aware large kernel convolutions")
  Member "knowing when to look adaptive attention via a visual sentinel for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "boosting image captioning with attributes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  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.CV(Computer Vision and Pattern Recognition)