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: "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
  Abstract: With the availability of large databases and recent improvements in deep learning methodology, the performance of AI systems is reaching or even exceeding the human level on an increasing number of complex tasks. Impressive examples of this development can be found in domains such as image classification, sentiment analysis, speech understanding or strategic game playing. However, because of their nested non-linear structure, these highly successful machine learning and artificial intelligence models are usually applied in a black box manner, i.e., no information is provided about what exactly makes them arrive at their predictions. Since this lack of transparency can be a major drawback, e.g., in medical applications, the development of methods for visualizing, explaining and interpreting deep learning models has recently attracted increasing attention. This paper summarizes recent developments in this field and makes a plea for more interpretability in artificial intelligence. Furthermore, it presents two approaches to explaining predictions of deep learning models, one method which computes the sensitivity of the prediction with respect to changes in the input and one approach which meaningfully decomposes the decision in terms of the input variables. These methods are evaluated on three classification tasks.

Connected through hyperedge HE-1 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda"):
  with members:
    - "ekiden a platform for confidentiality preserving trustworthy and performant smart contracts"
    - "energy recycling blockchain with proof of deep learning"
    - "decentralized and collaborative ai on blockchain"
    - "sok transparent dishonesty front running attacks on blockchain"
    - "reliable federated learning for mobile networks"
    - "keystone an open framework for architecting tees"
    - "tensorscone a secure tensorflow framework using intel sgx"
    - "coin ai a proof of useful work scheme for blockchain based distributed deep learning"

Connected through hyperedge HE-2 (induced by source paper "on the road with 16 neurons mental imagery with bio inspired deep neural networks"):
  with members:
    - "a survey of deep learning techniques for autonomous driving"
    - "neurall towards a unified model for visual perception in automated driving"
    - "yolov3 an incremental improvement"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "deep object centric policies for autonomous driving"
    - "recent advances in autoencoder based representation learning"
    - "advances in variational inference"
    - "explaining how a deep neural network trained with end to end learning steers a car"

Connected through hyperedge HE-3 (induced by source paper "fine grained uncertainty modeling in neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "visual interpretability for deep learning a survey"
    - "adversarial attacks and defences a survey"
    - "uncertainty aware ai ml why and how"
    - "evidential deep learning to quantify classification uncertainty"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-4 (induced by source paper "from data to actions in intelligent transportation systems a prescription of functional requirements for model actionability"):
  with members:
    - "generalizing from a few examples a survey on few shot learning"
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "predicting transportation modes of gps trajectories using feature engineering and noise removal"
    - "explanation methods in deep learning users values concerns and challenges"
    - "transferable pedestrian motion prediction models at intersections"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "understanding traffic density from large scale web camera data"
    - "when traffic flow prediction meets wireless big data analytics"

Connected through hyperedge HE-5 (induced by source paper "language is power representing states using natural language in reinforcement learning"):
  with members:
    - "hierarchical decision making by generating and following natural language instructions"
    - "action assembly sparse imitation learning for text based games with combinatorial action spaces"
    - "the natural language of actions"
    - "adaptive discretization for episodic reinforcement learning in metric spaces"
    - "textworld a learning environment for text based games"
    - "a comprehensive survey of deep learning for image captioning"
    - "emergence of invariance and disentanglement in deep representations"
    - "do latent tree learning models identify meaningful structure in sentences"

Connected through hyperedge HE-6 (induced by source paper "the emerging landscape of explainable ai planning and decision making"):
  with members:
    - "explainable reinforcement learning through a causal lens"
    - "generation of policy level explanations for reinforcement learning"
    - "expectation aware planning a unifying framework for synthesizing and executing self explaining plans for human aware planning"
    - "towards explainable ai planning as a service"
    - "exploring computational user models for agent policy summarization"
    - "explicability legibility predictability transparency privacy security the emerging landscape of interpretable agent behavior"
    - "contrastive explanation a structural model approach"
    - "learning finite state representations of recurrent policy networks"

Connected through hyperedge HE-7 (induced by source paper "convex density constraints for computing plausible counterfactual explanations"):
  with members:
    - "efficient computation of counterfactual explanations of lvq models"
    - "a survey on bias and fairness in machine learning"
    - "face feasible and actionable counterfactual explanations"
    - "interpretable counterfactual explanations guided by prototypes"
    - "a survey on explainable artificial intelligence xai towards medical xai"
    - "on the computation of counterfactual explanations a survey"
    - "generating contrastive explanations with monotonic attribute functions"
    - "local rule based explanations of black box decision systems"

Connected through hyperedge HE-8 (induced by source paper "lionforests local interpretation of random forests"):
  with members:
    - "lionets local interpretation of neural networks through penultimate layer decoding"
    - "explainable ai for trees from local explanations to global understanding"
    - "techniques for interpretable machine learning"
    - "a unified approach to interpreting model predictions"
    - "rule extraction algorithm for deep neural networks a review"
    - "xgboost a scalable tree boosting system"
    - "the mythos of model interpretability"
    - "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.AI(Artificial Intelligence)