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: "contrastive explanation a structural model approach"
  Abstract: The topic of causal explanation in artificial intelligence has gathered interest in recent years as researchers and practitioners aim to increase trust and understanding of intelligent decision-making and action. While different sub-fields have looked into this problem with a sub-field-specific view, there are few models that aim to capture explanation in AI more generally. One general model is based on structural causal models. It defines an explanation as a fact that, if found to be true, would constitute an actual cause of a specific event. However, research in philosophy and social sciences shows that explanations are contrastive: that is, when people ask for an explanation of an event -- the fact --- they (sometimes implicitly) are asking for an explanation relative to some contrast case; that is, "Why P rather than Q?". In this paper, we extend the structural causal model approach to define two complementary notions of contrastive explanation, and demonstrate them on two classical AI problems: classification and planning. We believe that this model can be used to define contrastive explanation of other subfield-specific AI models.

Connected through hyperedge HE-1 (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"
    - "learning finite state representations of recurrent policy networks"
    - "explanation in artificial intelligence insights from the social sciences"

Connected through hyperedge HE-2 (induced by source paper "distal explanations for explainable reinforcement learning agents"):
  with members:
    - "explainable reinforcement learning through a causal lens"
    - "conservative q improvement reinforcement learning for an interpretable decision tree policy"
    - "metrics for explainable ai challenges and prospects"
    - "contrastive explanations for reinforcement learning in terms of expected consequences"
    - "autonomous self explanation of behavior for interactive reinforcement learning agents"
    - "explanation in artificial intelligence insights from the social sciences"
    - "starcraft ii a new challenge for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "towards transparent robotic planning via contrastive explanations"):
  with members:
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "explaining explanations in ai"
    - "establishing appropriate trust via critical states"
    - "counterexamples for robotic planning explained in structured language"
    - "explanation in artificial intelligence insights from the social sciences"

Connected through hyperedge HE-4 (induced by source paper "generating contrastive explanations with monotonic attribute functions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "interpreting neural network judgments via minimal stable and symbolic corrections"
    - "methods for interpreting and understanding deep neural networks"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "tip typifying the interpretability of procedures"
    - "variational inference of disentangled latent concepts from unlabeled observations"

Connected through hyperedge HE-5 (induced by source paper "explainable reinforcement learning through a causal lens"):
  with members:
    - "a grounded interaction protocol for explainable artificial intelligence"
    - "metrics for explainable ai challenges and prospects"
    - "contrastive explanations for reinforcement learning in terms of expected consequences"
    - "autonomous self explanation of behavior for interactive reinforcement learning agents"
    - "explanation in artificial intelligence insights from the social sciences"
    - "starcraft ii a new challenge for reinforcement learning"
    - "counterfactuals and policy analysis in structural models"
    - "explanation trees for causal bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "measurable counterfactual local explanations for any classifier"):
  with members:
    - "explaining explanations in ai"
    - "network dissection quantifying interpretability of deep visual representations"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "explanation in artificial intelligence insights from the social sciences"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "a unified approach to interpreting model predictions"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-7 (induced by source paper "weight of evidence as a basis for human oriented explanations"):
  with members:
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "explanation in artificial intelligence insights from the social sciences"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "a unified approach to interpreting model predictions"
    - "density estimation using real nvp"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-8 (induced by source paper "towards explainable ai planning as a service"):
  with members:
    - "towards providing explanations for ai planner decisions"
    - "explanation in artificial intelligence insights from the social sciences"
    - "visualizations for an explainable planning agent"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"
    - "plan explicability and predictability for robot task planning"
    - "an approach to temporal planning and scheduling in domains with predictable exogenous events"

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)