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 "quality estimation for image captions based on large scale human evaluations"):
  Abstract: Automatic image captioning has improved significantly in the last few years, but the problem is far from being solved. Furthermore, while the standard automatic metrics, such as CIDEr and SPICE~\cite{cider,spice}, can be used for model selection, they cannot be used at inference-time given a previously unseen image since they require ground-truth references. In this paper, we focus on the related problem called Quality Estimation (QE) of image-captions. In contrast to automatic metrics, QE attempts to model caption quality without relying on ground-truth references. It can thus be applied as a second-pass model (after caption generation) to estimate the quality of captions even for previously unseen images. We conduct a large-scale human evaluation experiment, in which we collect a new dataset of more than 600k ratings of image-caption pairs. Using this dataset, we design and experiment with several QE modeling approaches and provide an analysis of their performance. Our results show that QE is feasible for image captioning.

Includes members:
  Member "vifidel evaluating the visual fidelity of image descriptions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reinforcing an image caption generator using off line human feedback")
  Member "informative image captioning with external sources of information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reinforcing an image caption generator using off line human feedback")
  Member "graph rise graph regularized image semantic embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "decoupled box proposal and featurization with ultrafine grained semantic labels improve image captioning and visual question answering")
  Member "decoupled box proposal and featurization with ultrafine grained semantic labels improve image captioning and visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "connecting vision and language with localized narratives")
  Member "supervised learning of universal sentence representations from natural language inference data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on contextual embeddings")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "bottom up and top down attention for image captioning and visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "inception v4 inception resnet and the impact of residual connections on learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")

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)