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: "automated rationale generation a technique for explainable ai and its effects on human perceptions"
  Abstract: Automated rationale generation is an approach for real-time explanation generation whereby a computational model learns to translate an autonomous agent's internal state and action data representations into natural language. Training on human explanation data can enable agents to learn to generate human-like explanations for their behavior. In this paper, using the context of an agent that plays Frogger, we describe (a) how to collect a corpus of explanations, (b) how to train a neural rationale generator to produce different styles of rationales, and (c) how people perceive these rationales. We conducted two user studies. The first study establishes the plausibility of each type of generated rationale and situates their user perceptions along the dimensions of confidence, humanlike-ness, adequate justification, and understandability. The second study further explores user preferences between the generated rationales with regard to confidence in the autonomous agent, communicating failure and unexpected behavior. Overall, we find alignment between the intended differences in features of the generated rationales and the perceived differences by users. Moreover, context permitting, participants preferred detailed rationales to form a stable mental model of the agent's behavior.

Connected through hyperedge HE-1 (induced by source paper "leveraging rationales to improve human task performance"):
  with members:
    - "interpreting cnns via decision trees"
    - "visual interpretability for deep learning a survey"
    - "what can ai do for me evaluating machine learning interpretations in cooperative play"
    - "interpreting cnn knowledge via an explanatory graph"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-2 (induced by source paper "human centered explainable ai towards a reflective sociotechnical approach"):
  with members:
    - "explanation in artificial intelligence insights from the social sciences"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "understanding neural networks through deep visualization"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (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"
    - "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"
    - "from what to how an initial review of publicly available ai ethics tools methods and research to translate principles into practices"

Connected through hyperedge HE-4 (induced by source paper "unexplainability and incomprehensibility of artificial intelligence"):
  with members:
    - "personal universes a solution to the multi agent value alignment problem"
    - "explaining explanations in ai"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "unpredictability of ai"
    - "techniques for interpretable machine learning"
    - "emergence of addictive behaviors in reinforcement learning agents"
    - "building safer agi by introducing artificial stupidity"
    - "manipulating and measuring model interpretability"

Connected through hyperedge HE-5 (induced by source paper "attention is not not explanation"):
  with members:
    - "human centered artificial intelligence and machine learning"
    - "attention is not explanation"
    - "generating token level explanations for natural language inference"
    - "explainable prediction of medical codes from clinical text"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "rationalizing neural predictions"
    - "the mythos of model interpretability"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-6 (induced by source paper "an interaction framework for studying co creative ai"):
  with members:
    - "friend collaborator student manager how design of an ai driven game level editor affects creators"
    - "co creative level design via machine learning"
    - "explainable pcgml via game design patterns"
    - "evaluating creativity in computational co creative systems"
    - "bringing impressionism to life with neural style transfer in come swim"
    - "event representations for automated story generation with deep neural nets"
    - "procedural content generation via machine learning pcgml"
    - "statistical parametric speech synthesis incorporating generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "automated let s play commentary"):
  with members:
    - "universal sentence encoder"
    - "towards automated let s play commentary"
    - "procedural content generation via machine learning pcgml"
    - "how the experts do it assessing and explaining agent behaviors in real time strategy games"
    - "resnet in resnet generalizing residual architectures"
    - "empirical evaluation of rectified activations in convolutional network"
    - "taking a deeper look at pedestrians"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "towards a robot explanation system a survey and our approach to state summarization storage and querying and human interface"):
  with members:
    - "animation techniques in human robot interaction user studies a systematic literature review"
    - "expressing robot incapability"
    - "visual interpretability for deep learning a survey"
    - "explanation in artificial intelligence insights from the social sciences"
    - "residual attention network for image classification"
    - "a review of verbal and non verbal human robot interactive communication"

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)