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: "interpreting visual question answering models"
  Abstract: Deep neural networks have shown striking progress and obtained state-of-the-art results in many AI research fields in the recent years. However, it is often unsatisfying to not know why they predict what they do. In this paper, we address the problem of interpreting Visual Question Answering (VQA) models. Specifically, we are interested in finding what part of the input (pixels in images or words in questions) the VQA model focuses on while answering the question. To tackle this problem, we use two visualization techniques -- guided backpropagation and occlusion -- to find important words in the question and important regions in the image. We then present qualitative and quantitative analyses of these importance maps.

Connected through hyperedge HE-1 (induced by source paper "explanation in human ai systems a literature meta review synopsis of key ideas and publications and bibliography for explainable ai"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "ted teaching ai to explain its decisions"
    - "explaining explanations an overview of interpretability of machine learning"
    - "deep learning a critical appraisal"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "explainable artificial intelligence xai the goodness criteria and the grasp ability test"
    - "interpretable explorable approximations of black box models"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "taking a hint leveraging explanations to make vision and language models more grounded"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "women also snowboard overcoming bias in captioning models"
    - "neural baby talk"
    - "choose your neuron incorporating domain knowledge through neuron importance"
    - "overcoming language priors in visual question answering with adversarial regularization"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "axiomatic attribution for deep networks"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-3 (induced by source paper "vector field neural networks"):
  with members:
    - "stable architectures for deep neural networks"
    - "the numerics of gans"
    - "maximum principle based algorithms for deep learning"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"
    - "reversible architectures for arbitrarily deep residual neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "choose your neuron incorporating domain knowledge through neuron importance"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "understanding convolutional networks with apple automatic patch pattern labeling for explanation"
    - "semantic autoencoder for zero shot learning"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "network dissection quantifying interpretability of deep visual representations"
    - "nisp pruning networks using neuron importance score propagation"
    - "zero shot learning the good the bad and the ugly"
    - "less is more zero shot learning from online textual documents with noise suppression"

Connected through hyperedge HE-5 (induced by source paper "vector field based neural networks"):
  with members:
    - "the numerics of gans"
    - "understanding neural networks through deep visualization"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "what s in a question using visual questions as a form of supervision"):
  with members:
    - "training recurrent answering units with joint loss minimization for vqa"
    - "leveraging visual question answering for image caption ranking"
    - "a focused dynamic attention model for visual question answering"
    - "ambient sound provides supervision for visual learning"
    - "dualnet domain invariant network for visual question answering"
    - "segmentation from natural language expressions"
    - "spice semantic propositional image caption evaluation"
    - "multimodal residual learning for visual qa"

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.CV(Computer Vision and Pattern Recognition)