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: "interpretable and pedagogical examples"
  Abstract: Teachers intentionally pick the most informative examples to show their students. However, if the teacher and student are neural networks, the examples that the teacher network learns to give, although effective at teaching the student, are typically uninterpretable. We show that training the student and teacher iteratively, rather than jointly, can produce interpretable teaching strategies. We evaluate interpretability by (1) measuring the similarity of the teacher's emergent strategies to intuitive strategies in each domain and (2) conducting human experiments to evaluate how effective the teacher's strategies are at teaching humans. We show that the teacher network learns to select or generate interpretable, pedagogical examples to teach rule-based, probabilistic, boolean, and hierarchical concepts.

Connected through hyperedge HE-1 (induced by source paper "unexplainability and incomprehensibility of artificial intelligence"):
  with members:
    - "automated rationale generation a technique for explainable ai and its effects on human perceptions"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "invenio discovering hidden relationships between tasks domains using structured meta learning"):
  with members:
    - "self supervised generalisation with meta auxiliary learning"
    - "machine learning on biomedical images interactive learning transfer learning class imbalance and beyond"
    - "the imaterialist fashion attribute dataset"
    - "task2vec task embedding for meta learning"
    - "the information complexity of learning tasks their structure and their distance"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "taskonomy disentangling task transfer learning"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "learning to teach with dynamic loss functions"):
  with members:
    - "neural architecture optimization"
    - "adversarial neural machine translation"
    - "focal loss for dense object detection"
    - "attention is all you need"
    - "classical structured prediction losses for sequence to sequence learning"
    - "convolutional sequence to sequence learning"
    - "learning to optimize"
    - "densely connected convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "controlled random search improves sample mining and hyper parameter optimization"):
  with members:
    - "sampling matters in deep embedding learning"
    - "critical hyper parameters no random no cry"
    - "importance sampling for minibatches"
    - "adam a method for stochastic optimization"
    - "a bayesian sampling approach to exploration in reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "interactive reinforcement learning for object grounding via self talking"):
  with members:
    - "evaluating visual conversational agents via cooperative human ai games"
    - "emergent communication in a multi modal multi step referential game"
    - "natural language does not emerge naturally in multi agent dialog"
    - "emergence of grounded compositional language in multi agent populations"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "guesswhat visual object discovery through multi modal dialogue"

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)