human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "measuring machine intelligence through visual question answering"
  Abstract: As machines have become more intelligent, there has been a renewed interest in methods for measuring their intelligence. A common approach is to propose tasks for which a human excels, but one which machines find difficult. However, an ideal task should also be easy to evaluate and not be easily gameable. We begin with a case study exploring the recently popular task of image captioning and its limitations as a task for measuring machine intelligence. An alternative and more promising task is Visual Question Answering that tests a machine's ability to reason about language and vision. We describe a dataset unprecedented in size created for the task that contains over 760,000 human generated questions about images. Using around 10 million human generated answers, machines may be easily evaluated.

Connected through hyperedge HE-1 (induced by source paper "straight to the facts learning knowledge base retrieval for factual visual question answering"):
  with members:
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "learning to reason end to end module networks for visual question answering"
    - "creativity generating diverse questions using variational autoencoders"
    - "high order attention models for visual question answering"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "image captioning and visual question answering based on attributes and external knowledge"
    - "multimodal residual learning for visual qa"

Connected through hyperedge HE-2 (induced by source paper "out of the box reasoning with graph convolution nets for factual visual question answering"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "visual question generation as dual task of visual question answering"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "creativity generating diverse questions using variational autoencoders"
    - "high order attention models for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "multimodal explanations justifying decisions and pointing to the evidence"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpreting deep visual representations via network dissection"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "hadamard product for low rank bilinear pooling"
    - "spice semantic propositional image caption evaluation"
    - "learning what and where to draw"
    - "generating visual explanations"

Connected through hyperedge HE-4 (induced by source paper "creativity generating diverse questions using variational autoencoders"):
  with members:
    - "improved techniques for training gans"
    - "multimodal residual learning for visual qa"
    - "diverse beam search decoding diverse solutions from neural sequence models"
    - "image captioning and visual question answering based on attributes and their related external knowledge"
    - "hierarchical question image co attention for visual question answering"
    - "generating natural questions about an image"
    - "revisiting visual question answering baselines"
    - "dynamic memory networks for visual and textual question answering"

Connected through hyperedge HE-5 (induced by source paper "the promise of premise harnessing question premises in visual question answering"):
  with members:
    - "c vqa a compositional split of the visual question answering vqa v1 0 dataset"
    - "scene graph generation by iterative message passing"
    - "attentive explanations justifying decisions and pointing to the evidence"
    - "spice semantic propositional image caption evaluation"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "multimodal residual learning for visual qa"
    - "hierarchical question image co attention for visual question answering"
    - "question relevance in vqa identifying non visual and false premise questions"

Connected through hyperedge HE-6 (induced by source paper "vse improving visual semantic embeddings with hard negatives"):
  with members:
    - "sampling matters in deep embedding learning"
    - "learning two branch neural networks for image text matching tasks"
    - "dual attention networks for multimodal reasoning and matching"
    - "instance aware image and sentence matching with selective multimodal lstm"
    - "linking image and text with 2 way nets"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "maximum margin structured learning with deep networks for 3d human pose estimation"

Connected through hyperedge HE-7 (induced by source paper "hierarchical question image co attention for visual question answering"):
  with members:
    - "attentive pooling networks"
    - "a focused dynamic attention model for visual question answering"
    - "multimodal residual learning for visual qa"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "dynamic memory networks for visual and textual question answering"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "visual7w grounded question answering in images"
    - "exploring models and data for image question answering"

Question: Please tell me which class the center node 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.AI(Artificial Intelligence)