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: "what do we need to build explainable ai systems for the medical domain"
  Abstract: Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate impressive practical success in many different application domains, e.g. in autonomous driving, speech recognition, or recommender systems. Deep learning approaches, trained on extremely large data sets or using reinforcement learning methods have even exceeded human performance in visual tasks, particularly on playing games such as Atari, or mastering the game of Go. Even in the medical domain there are remarkable results. The central problem of such models is that they are regarded as black-box models and even if we understand the underlying mathematical principles, they lack an explicit declarative knowledge representation, hence have difficulty in generating the underlying explanatory structures. This calls for systems enabling to make decisions transparent, understandable and explainable. A huge motivation for our approach are rising legal and privacy aspects. The new European General Data Protection Regulation entering into force on May 25th 2018, will make black-box approaches difficult to use in business. This does not imply a ban on automatic learning approaches or an obligation to explain everything all the time, however, there must be a possibility to make the results re-traceable on demand. In this paper we outline some of our research topics in the context of the relatively new area of explainable-AI with a focus on the application in medicine, which is a very special domain. This is due to the fact that medical professionals are working mostly with distributed heterogeneous and complex sources of data. In this paper we concentrate on three sources: images, *omics data and text. We argue that research in explainable-AI would generally help to facilitate the implementation of AI/ML in the medical domain, and specifically help to facilitate transparency and trust.

Connected through hyperedge HE-1 (induced by source paper "foundations of explainable knowledge enabled systems"):
  with members:
    - "one explanation does not fit all a toolkit and taxonomy of ai explainability techniques"
    - "explaining explanations in ai"
    - "knowledge integration for disease characterization a breast cancer example"
    - "causal learning and explanation of deep neural networks via autoencoded activations"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "network dissection quantifying interpretability of deep visual representations"
    - "explanation in artificial intelligence insights from the social sciences"
    - "interpreting deep visual representations via network dissection"

Connected through hyperedge HE-2 (induced by source paper "explainable artificial intelligence and machine learning a reality rooted perspective"):
  with members:
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "explaining explanations in ai"
    - "a survey on explainable artificial intelligence xai towards medical xai"
    - "explaining explanations an overview of interpretability of machine learning"
    - "what does explainable ai really mean a new conceptualization of perspectives"
    - "the mythos of model interpretability"
    - "building machines that learn and think like people"

Connected through hyperedge HE-3 (induced by source paper "explainability in human agent systems"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "visual interpretability for deep learning a survey"
    - "methods for interpreting and understanding deep neural networks"
    - "textual explanations for self driving vehicles"
    - "a human grounded evaluation benchmark for local explanations of machine learning"
    - "explainable neural computation via stack neural module networks"
    - "providing explanations for recommendations in reciprocal environments"
    - "explainable software analytics"

Connected through hyperedge HE-4 (induced by source paper "explaining models by propagating shapley values of local components"):
  with members:
    - "explaining deep neural networks with a polynomial time algorithm for shapley values approximation"
    - "generalized integrated gradients a practical method for explaining diverse ensembles"
    - "learning explainable models using attribution priors"
    - "the many shapley values for model explanation"
    - "explainable ai for trees from local explanations to global understanding"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"

Connected through hyperedge HE-5 (induced by source paper "evaluation of neural network uncertainty estimation with application to resource constrained platforms"):
  with members:
    - "towards increased trustworthiness of deep learning segmentation methods on cardiac mri"
    - "leveraging uncertainty estimates for predicting segmentation quality"
    - "learning for single shot confidence calibration in deep neural networks through stochastic inferences"
    - "confidence scoring using whitebox meta models with linear classifier probes"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "distance based confidence score for neural network classifiers"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-6 (induced by source paper "parallel medical imaging a new data knowledge driven evolutionary framework for medical image analysis"):
  with members:
    - "melanogans high resolution skin lesion synthesis with gans"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "icadx interpretable computer aided diagnosis of breast masses"
    - "gans for medical image analysis"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "image to image translation with conditional adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-7 (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"
    - "attentive explanations justifying decisions and pointing to the evidence"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "kandinsky patterns"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "a glass box interactive machine learning approach for solving np hard problems with the human in the loop"
    - "generating visual explanations"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"

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)