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 "deconfounded image captioning a causal retrospect"):
  Abstract: The dataset bias in vision-language tasks is becoming one of the main problems that hinder the progress of our community. However, recent studies lack a principled analysis of the bias. In this paper, we present a novel perspective: Deconfounded Image Captioning (DIC), to find out the cause of the bias in image captioning, then retrospect modern neural image captioners, and finally propose a DIC framework: DICv1.0. DIC is based on causal inference, whose two principles: the backdoor and front-door adjustments, help us to review previous works and design the effective models. In particular, we showcase that DICv1.0 can strengthen two prevailing captioning models and achieves a single-model 130.7 CIDEr-D and 128.4 c40 CIDEr-D on Karpathy split and online split of the challenging MS-COCO dataset, respectively. Last but not least, DICv1.0 is merely a natural derivation from our causal retrospect, which opens a promising direction for image captioning.

Includes members:
  Member "learning to collocate neural modules for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mutatt visual textual mutual guidance for referring expression comprehension")
  Member "hierarchy parsing for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "vision and language from visual perception to content creation")
  Member "attention on attention for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "normalized and geometry aware self attention network for image captioning")
  Member "rubi reducing unimodal biases in visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unbiased scene graph generation from biased training")
  Member "gqa a new dataset for real world visual reasoning and compositional question answering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unbiased scene graph generation from biased training")
  Member "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "unified vision language pre training for image captioning and vqa":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "in defense of grid features for visual question answering")
  Member "context aware visual policy network for fine grained image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to collocate neural modules for image captioning")

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)