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: "rationalization a neural machine translation approach to generating natural language explanations"
  Abstract: We introduce AI rationalization, an approach for generating explanations of autonomous system behavior as if a human had performed the behavior. We describe a rationalization technique that uses neural machine translation to translate internal state-action representations of an autonomous agent into natural language. We evaluate our technique in the Frogger game environment, training an autonomous game playing agent to rationalize its action choices using natural language. A natural language training corpus is collected from human players thinking out loud as they play the game. We motivate the use of rationalization as an approach to explanation generation and show the results of two experiments evaluating the effectiveness of rationalization. Results of these evaluations show that neural machine translation is able to accurately generate rationalizations that describe agent behavior, and that rationalizations are more satisfying to humans than other alternative methods of explanation.

Connected through hyperedge HE-1 (induced by source paper "friend collaborator student manager how design of an ai driven game level editor affects creators"):
  with members:
    - "co creative level design via machine learning"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "teaching meaningful explanations"
    - "procedural content generation via machine learning pcgml"
    - "a neural representation of sketch drawings"
    - "semantic style transfer and turning two bit doodles into fine artworks"
    - "deep learning for music"
    - "text based lstm networks for automatic music composition"

Connected through hyperedge HE-2 (induced by source paper "extractive adversarial networks high recall explanations for identifying personal attacks in social media posts"):
  with members:
    - "detecting deceptive reviews using generative adversarial networks"
    - "explaining explanations an overview of interpretability of machine learning"
    - "a survey of methods for explaining black box models"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "binary generative adversarial networks for image retrieval"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"

Connected through hyperedge HE-3 (induced by source paper "explainable pcgml via game design patterns"):
  with members:
    - "teaching meaningful explanations"
    - "procedural content generation via machine learning pcgml"
    - "the vglc the video game level corpus"
    - "super mario as a string platformer level generation via lstms"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-4 (induced by source paper "explain yourself a natural language interface for scrutable autonomous robots"):
  with members:
    - "the orca hub explainable offshore robotics through intelligent interfaces"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"
    - "miriam a multimodal chat based interface for autonomous systems"

Connected through hyperedge HE-5 (induced by source paper "explaining transition systems through program induction"):
  with members:
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "robustfill neural program learning under noisy i o"
    - "making neural programming architectures generalize via recursion"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "a syntactic neural model for general purpose code generation"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "graying the black box understanding dqns"
    - "deepcoder learning to write programs"

Connected through hyperedge HE-6 (induced by source paper "using program induction to interpret transition system dynamics"):
  with members:
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "robustfill neural program learning under noisy i o"
    - "making neural programming architectures generalize via recursion"
    - "a syntactic neural model for general purpose code generation"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "deepcoder learning to write programs"
    - "generating visual explanations"
    - "scalable bayesian rule lists"

Connected through hyperedge HE-7 (induced by source paper "guiding reinforcement learning exploration using natural language"):
  with members:
    - "effective approaches to attention based neural machine translation"

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)