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 "ivqa inverse visual question answering"):
  Abstract: We propose the inverse problem of Visual question answering (iVQA), and explore its suitability as a benchmark for visuo-linguistic understanding. The iVQA task is to generate a question that corresponds to a given image and answer pair. Since the answers are less informative than the questions, and the questions have less learnable bias, an iVQA model needs to better understand the image to be successful than a VQA model. We pose question generation as a multi-modal dynamic inference process and propose an iVQA model that can gradually adjust its focus of attention guided by both a partially generated question and the answer. For evaluation, apart from existing linguistic metrics, we propose a new ranking metric. This metric compares the ground truth question's rank among a list of distractors, which allows the drawbacks of different algorithms and sources of error to be studied. Experimental results show that our model can generate diverse, grammatically correct and content correlated questions that match the given answer.

Includes members:
  Member "image grounded conversations multimodal context for natural question and response generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "low resource knowledge grounded dialogue generation")
  Member "hadamard product for low rank bilinear pooling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "component analysis for visual question answering architectures")
  Member "analyzing the behavior of visual question answering models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "knowing when to look adaptive attention via a visual sentinel for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "semantic regularisation for recurrent image annotation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improved hard example mining by discovering attribute based hard person identity")
  Member "generating natural questions about an image":
    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 "automatic generation of grounded visual questions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "image caption generation with text conditional semantic attention":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on architectures for including visual information in neural language models for image description")

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)