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: "attentive explanations justifying decisions and pointing to the evidence"
  Abstract: Deep models are the defacto standard in visual decision models due to their impressive performance on a wide array of visual tasks. However, they are frequently seen as opaque and are unable to explain their decisions. In contrast, humans can justify their decisions with natural language and point to the evidence in the visual world which led to their decisions. We postulate that deep models can do this as well and propose our Pointing and Justification (PJ-X) model which can justify its decision with a sentence and point to the evidence by introspecting its decision and explanation process using an attention mechanism. Unfortunately there is no dataset available with reference explanations for visual decision making. We thus collect two datasets in two domains where it is interesting and challenging to explain decisions. First, we extend the visual question answering task to not only provide an answer but also a natural language explanation for the answer. Second, we focus on explaining human activities which is traditionally more challenging than object classification. We extensively evaluate our PJ-X model, both on the justification and pointing tasks, by comparing it to prior models and ablations using both automatic and human evaluations.

Connected through hyperedge HE-1 (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-2 (induced by source paper "understanding artificial intelligence ethics and safety"):
  with members:
    - "a roadmap toward the resilient internet of things for cyber physical systems"
    - "a grounded interaction protocol for explainable artificial intelligence"
    - "automated rationale generation a technique for explainable ai and its effects on human perceptions"
    - "model cards for model reporting"
    - "a framework for understanding unintended consequences of machine learning"
    - "spectrum data poisoning with adversarial deep learning"
    - "explaining explanations in ai"
    - "problem formulation and fairness"

Connected through hyperedge HE-3 (induced by source paper "visual entailment a novel task for fine grained image understanding"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "bilinear attention networks"
    - "annotation artifacts in natural language inference data"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "visual reasoning by progressive module networks"
    - "grounded textual entailment"
    - "compositional attention networks for machine reasoning"

Connected through hyperedge HE-4 (induced by source paper "improving transparency of deep neural inference process"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "methods for interpreting and understanding deep neural networks"
    - "open problems in engineering and quality assurance of safety critical machine learning systems"
    - "visual analytics for explainable deep learning"
    - "explainable software analytics"
    - "network dissection quantifying interpretability of deep visual representations"
    - "explaining nonlinear classification decisions with deep taylor decomposition"

Connected through hyperedge HE-5 (induced by source paper "assisting human experts in the interpretation of their visual process a case study on assessing copper surface adhesive potency"):
  with members:
    - "boosting segmentation with weak supervision from image to image translation"
    - "a multi task u net for segmentation with lazy labels"
    - "knowing where to look analysis on attention of visual question answering system"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "deep convolutional neural network design patterns"
    - "image captioning with semantic attention"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"
    - "understanding neural networks through deep visualization"

Connected through hyperedge HE-6 (induced by source paper "understanding attention and generalization in graph neural networks"):
  with members:
    - "graph u nets"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "hierarchical graph representation learning with differentiable pooling"
    - "attention models in graphs a survey"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "how powerful are graph neural networks"
    - "attention is all you need"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"

Connected through hyperedge HE-7 (induced by source paper "an ontology based approach to explaining artificial neural networks"):
  with members:
    - "metrics for explainable ai challenges and prospects"
    - "what is an ontology"
    - "explainable recommendation a survey and new perspectives"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "repairing ontologies via axiom weakening"
    - "interpreting blackbox models via model extraction"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-8 (induced by source paper "diagnostic accuracy of content based dermatoscopic image retrieval with deep classification features"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "what do we need to build explainable ai systems for the medical domain"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"

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)