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: "do explanations make vqa models more predictable to a human"
  Abstract: A rich line of research attempts to make deep neural networks more transparent by generating human-interpretable 'explanations' of their decision process, especially for interactive tasks like Visual Question Answering (VQA). In this work, we analyze if existing explanations indeed make a VQA model -- its responses as well as failures -- more predictable to a human. Surprisingly, we find that they do not. On the other hand, we find that human-in-the-loop approaches that treat the model as a black-box do.

Connected through hyperedge HE-1 (induced by source paper "challenges and prospects in vision and language research"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "vqd visual query detection in natural scenes"
    - "answer them all toward universal visual question answering models"
    - "bilinear attention networks"
    - "end to end image captioning exploits multimodal distributional similarity"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "the natural language decathlon multitask learning as question answering"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"

Connected through hyperedge HE-2 (induced by source paper "lucid explanations help using a human ai image guessing game to evaluate machine explanation helpfulness"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "it takes two to tango towards theory of ai s mind"

Connected through hyperedge HE-3 (induced by source paper "on human predictions with explanations and predictions of machine learning models a case study on deception detection"):
  with members:
    - "what can ai do for me evaluating machine learning interpretations in cooperative play"
    - "teaching categories to human learners with visual explanations"
    - "a unified approach to interpreting model predictions"
    - "algorithmic decision making and the cost of fairness"
    - "improving human machine cooperative visual search with soft highlighting"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"
    - "rationalizing neural predictions"

Connected through hyperedge HE-4 (induced by source paper "generating user friendly explanations for loan denials using gans"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "adversarially regularized autoencoders for generating discrete structures"
    - "the promise and peril of human evaluation for model interpretability"
    - "what does explainable ai really mean a new conceptualization of perspectives"
    - "accountability of ai under the law the role of explanation"
    - "causalgan learning causal implicit generative models with adversarial training"

Connected through hyperedge HE-5 (induced by source paper "interpreting cnns via decision trees"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "visual interpretability for deep learning a survey"
    - "unsupervised learning of neural networks to explain neural networks"
    - "network transplanting"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "interpreting cnn knowledge via an explanatory graph"
    - "dynamic routing between capsules"

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)