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: "towards a grounded dialog model for explainable artificial intelligence"
  Abstract: To generate trust with their users, Explainable Artificial Intelligence (XAI) systems need to include an explanation model that can communicate the internal decisions, behaviours and actions to the interacting humans. Successful explanation involves both cognitive and social processes. In this paper we focus on the challenge of meaningful interaction between an explainer and an explainee and investigate the structural aspects of an explanation in order to propose a human explanation dialog model. We follow a bottom-up approach to derive the model by analysing transcripts of 398 different explanation dialog types. We use grounded theory to code and identify key components of which an explanation dialog consists. We carry out further analysis to identify the relationships between components and sequences and cycles that occur in a dialog. We present a generalized state model obtained by the analysis and compare it with an existing conceptual dialog model of explanation.

Connected through hyperedge HE-1 (induced by source paper "ai for explaining decisions in multi agent environments"):
  with members:
    - "explainability in human agent systems"
    - "complementary reinforcement learning towards explainable agents"
    - "how model accuracy and explanation fidelity influence user trust"
    - "on the relative expressiveness of bayesian and neural networks"
    - "representation justification and explanation in a value driven agent an argumentation based approach"
    - "human satisfaction as the ultimate goal in ridesharing"
    - "local interpretable model agnostic explanations of bayesian predictive models via kullback leibler projections"
    - "providing explanations for recommendations in reciprocal environments"

Connected through hyperedge HE-2 (induced by source paper "a multi task deep learning model for the classification of age related macular degeneration"):
  with members:
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "a survey on deep learning in medical image analysis"
    - "a survey on multi task learning"
    - "multi task video captioning with video and entailment generation"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "representation justification and explanation in a value driven agent an argumentation based approach"):
  with members:
    - "building jiminy cricket an architecture for moral agreements among stakeholders"
    - "a symbolic approach to explaining bayesian network classifiers"
    - "explanation in artificial intelligence insights from the social sciences"
    - "prioritized norms in formal argumentation"
    - "defeasible logic programming an argumentative approach"

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)