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 "quantifying and alleviating the language prior problem in visual question answering"):
  Abstract: Benefiting from the advancement of computer vision, natural language processing and information retrieval techniques, visual question answering (VQA), which aims to answer questions about an image or a video, has received lots of attentions over the past few years. Although some progress has been achieved so far, several studies have pointed out that current VQA models are heavily affected by the \emph{language prior problem}, which means they tend to answer questions based on the co-occurrence patterns of question keywords (e.g., how many) and answers (e.g., 2) instead of understanding images and questions. Existing methods attempt to solve this problem by either balancing the biased datasets or forcing models to better understand images. However, only marginal effects and even performance deterioration are observed for the first and second solution, respectively. In addition, another important issue is the lack of measurement to quantitatively measure the extent of the language prior effect, which severely hinders the advancement of related techniques. #R##N#In this paper, we make contributions to solve the above problems from two perspectives. Firstly, we design a metric to quantitatively measure the language prior effect of VQA models. The proposed metric has been demonstrated to be effective in our empirical studies. Secondly, we propose a regularization method (i.e., score regularization module) to enhance current VQA models by alleviating the language prior problem as well as boosting the backbone model performance. The proposed score regularization module adopts a pair-wise learning strategy, which makes the VQA models answer the question based on the reasoning of the image (upon this question) instead of basing on question-answer patterns observed in the biased training set. The score regularization module is flexible to be integrated into various VQA models.

Includes members:
  Member "review driven answer generation for product related questions in e commerce":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving multi turn dialogue modelling with utterance rewriter")
  Member "mmalfm explainable recommendation by leveraging reviews and images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "estimation action reflection towards deep interaction between conversational and recommender systems")
  Member "learning to count objects in natural images for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "in defense of grid features for visual question answering")
  Member "improved fusion of visual and language representations by dense symmetric co attention for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "see more know more unsupervised video object segmentation with co attention siamese networks")
  Member "overcoming language priors in visual question answering with adversarial regularization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "counterfactual samples synthesizing for robust visual question answering")
  Member "visual question answering datasets algorithms and future challenges":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a question centric model for visual question answering in medical imaging")
  Member "show ask attend and answer a strong baseline for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cq vqa visual question answering on categorized questions")
  Member "don t just assume look and answer overcoming priors for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unbiased scene graph generation from biased training")

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)