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: "generating visual explanations"
  Abstract: Clearly explaining a rationale for a classification decision to an end-user can be as important as the decision itself. Existing approaches for deep visual recognition are generally opaque and do not output any justification text; contemporary vision-language models can describe image content but fail to take into account class-discriminative image aspects which justify visual predictions. We propose a new model that focuses on the discriminating properties of the visible object, jointly predicts a class label, and explains why the predicted label is appropriate for the image. We propose a novel loss function based on sampling and reinforcement learning that learns to generate sentences that realize a global sentence property, such as class specificity. Our results on a fine-grained bird species classification dataset show that our model is able to generate explanations which are not only consistent with an image but also more discriminative than descriptions produced by existing captioning methods.

Connected through hyperedge HE-1 (induced by source paper "explaining with counter visual attributes and examples"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "interpreting adversarially trained convolutional neural networks"
    - "counterfactual visual explanations"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "a large scale attribute dataset for zero shot learning"
    - "techniques for interpretable machine learning"

Connected through hyperedge HE-2 (induced by source paper "chexplain enabling physicians to explore and understanddata driven ai enabled medical imaging analysis"):
  with members:
    - "outlining the design space of explainable intelligent systems for medical diagnosis"
    - "human centered tools for coping with imperfect algorithms during medical decision making"
    - "explanation methods in deep learning users values concerns and challenges"
    - "explainable cardiac pathology classification on cine mri with motion characterization by semi supervised learning of apparent flow"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "generating counterfactual explanations with natural language"
    - "towards interpretable r cnn by unfolding latent structures"
    - "explanation in artificial intelligence insights from the social sciences"

Connected through hyperedge HE-3 (induced by source paper "a study on multimodal and interactive explanations for visual question answering"):
  with members:
    - "generating natural language explanations for visual question answering using scene graphs and visual attention"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-4 (induced by source paper "explainable deep modeling of tabular data using tablegraphnet"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "this looks like that deep learning for interpretable image recognition"
    - "deep sets"
    - "relational inductive biases deep learning and graph networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"

Connected through hyperedge HE-5 (induced by source paper "evaluating saliency map explanations for convolutional neural networks a user study"):
  with members:
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "minimalistic explanations capturing the essence of decisions"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "what can ai do for me evaluating machine learning interpretations in cooperative play"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "manipulating and measuring model interpretability"
    - "explanation in artificial intelligence insights from the social sciences"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"

Connected through hyperedge HE-6 (induced by source paper "factors influencing perceived fairness in algorithmic decision making algorithm outcomes development procedures and individual differences"):
  with members:
    - "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "society in the loop programming the algorithmic social contract"
    - "the frontiers of fairness in machine learning"
    - "manipulating and measuring model interpretability"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "a reductions approach to fair classification"
    - "demographics and discussion influence views on algorithmic fairness"

Connected through hyperedge HE-7 (induced by source paper "keeping community in the loop understanding wikipedia stakeholder values for machine learning based systems"):
  with members:
    - "the frontiers of fairness in machine learning"
    - "an empirical study of rich subgroup fairness for machine learning"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "on fairness and calibration"
    - "network dissection quantifying interpretability of deep visual representations"
    - "explanation in artificial intelligence insights from the social sciences"
    - "operationalizing conflict and cooperation between automated software agents in wikipedia a replication and expansion of even good bots fight"
    - "algorithmic decision making and the cost of fairness"

Connected through hyperedge HE-8 (induced by source paper "learning global transparent models from local contrastive explanations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "model agnostic contrastive explanations for structured data"
    - "improving simple models with confidence profiles"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "a unified approach to interpreting model predictions"
    - "interpreting blackbox models via model extraction"
    - "why should i trust you explaining the predictions of any classifier"

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)