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: "hard to cheat a turing test based on answering questions about images"
  Abstract: Progress in language and image understanding by machines has sparkled the interest of the research community in more open-ended, holistic tasks, and refueled an old AI dream of building intelligent machines. We discuss a few prominent challenges that characterize such holistic tasks and argue for "question answering about images" as a particular appealing instance of such a holistic task. In particular, we point out that it is a version of a Turing Test that is likely to be more robust to over-interpretations and contrast it with tasks like grounding and generation of descriptions. Finally, we discuss tools to measure progress in this field.

Connected through hyperedge HE-1 (induced by source paper "assessing the robustness of visual question answering"):
  with members:
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "robustness via curvature regularization and vice versa"
    - "visual question answering datasets algorithms and future challenges"
    - "mutan multimodal tucker fusion for visual question answering"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "an analysis of visual question answering algorithms"
    - "hadamard product for low rank bilinear pooling"
    - "ask your neurons a deep learning approach to visual question answering"

Connected through hyperedge HE-2 (induced by source paper "learning visual question answering by bootstrapping hard attention"):
  with members:
    - "hyperbolic attention networks"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "self attention with relative position representations"
    - "object based reasoning in vqa"
    - "relational inductive biases deep learning and graph networks"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "ddrprog a clevr differentiable dynamic reasoning programmer"

Connected through hyperedge HE-3 (induced by source paper "ask your neurons a deep learning approach to visual question answering"):
  with members:
    - "hadamard product for low rank bilinear pooling"
    - "a focused dynamic attention model for visual question answering"
    - "hierarchical co attention for visual question answering"
    - "dualnet domain invariant network for visual question answering"
    - "segmentation from natural language expressions"
    - "tutorial on answering questions about images with deep learning"
    - "grounding of textual phrases in images by reconstruction"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"

Connected through hyperedge HE-4 (induced by source paper "mean box pooling a rich image representation and output embedding for the visual madlibs task"):
  with members:
    - "person recognition in personal photo collections"
    - "ask your neurons a deep learning approach to visual question answering"
    - "dynamic memory networks for visual and textual question answering"
    - "visual madlibs fill in the blank image generation and question answering"
    - "learning to answer questions from image using convolutional neural network"
    - "ask attend and answer exploring question guided spatial attention for visual question answering"
    - "stacked attention networks for image question answering"
    - "where to look focus regions for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "tutorial on answering questions about images with deep learning"):
  with members:
    - "a focused dynamic attention model for visual question answering"
    - "ask your neurons a deep learning approach to visual question answering"
    - "hierarchical question image co attention for visual question answering"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "neural module networks"
    - "ask your neurons a neural based approach to answering questions about images"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "what value do explicit high level concepts have in vision to language problems"):
  with members:
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "hcp a flexible cnn framework for multi label image classification"
    - "image captioning with semantic attention"
    - "language models for image captioning the quirks and what works"
    - "vqa visual question answering"
    - "aligning where to see and what to tell image caption with region based attention and scene factorization"
    - "show attend and tell neural image caption generation with visual attention"
    - "learning to answer questions from image using convolutional neural network"

Connected through hyperedge HE-7 (induced by source paper "what value high level concepts in vision to language problems"):
  with members:
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "hcp a flexible cnn framework for multi label image classification"
    - "aligning where to see and what to tell image caption with region based attention and scene factorization"
    - "learning to answer questions from image using convolutional neural network"
    - "ask your neurons a neural based approach to answering questions about images"
    - "microsoft coco captions data collection and evaluation server"
    - "guiding long short term memory for image caption generation"
    - "are you talking to a machine dataset and methods for multilingual image question answering"

Connected through hyperedge HE-8 (induced by source paper "learning to answer questions from image using convolutional neural network"):
  with members:
    - "exploring models and data for image question answering"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "show attend and tell neural image caption generation with visual attention"
    - "convolutional neural network architectures for matching natural language sentences"
    - "ask your neurons a neural based approach to answering questions about images"
    - "are you talking to a machine dataset and methods for multilingual image question answering"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

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)