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: "the continuous hint factory providing hints in vast and sparsely populated edit distance spaces"
  Abstract: Intelligent tutoring systems can support students in solving multi-step tasks by providing hints regarding what to do next. However, engineering such next-step hints manually or via an expert model becomes infeasible if the space of possible states is too large. Therefore, several approaches have emerged to infer next-step hints automatically, relying on past students' data. In particular, the Hint Factory (Barnes & Stamper, 2008) recommends edits that are most likely to guide students from their current state towards a correct solution, based on what successful students in the past have done in the same situation. Still, the Hint Factory relies on student data being available for any state a student might visit while solving the task, which is not the case for some learning tasks, such as open-ended programming tasks. In this contribution we provide a mathematical framework for edit-based hint policies and, based on this theory, propose a novel hint policy to provide edit hints in vast and sparsely populated state spaces. In particular, we extend the Hint Factory by considering data of past students in all states which are similar to the student's current state and creating hints approximating the weighted average of all these reference states. Because the space of possible weighted averages is continuous, we call this approach the Continuous Hint Factory. In our experimental evaluation, we demonstrate that the Continuous Hint Factory can predict more accurately what capable students would do compared to existing prediction schemes on two learning tasks, especially in an open-ended programming task, and that the Continuous Hint Factory is comparable to existing hint policies at reproducing tutor hints on a simple UML diagram task.

Connected through hyperedge HE-1 (induced by source paper "a survey of automated programming hint generation the hints framework"):
  with members:
    - "automatic software repair a bibliography"
    - "deep reinforcement learning for programming language correction"
    - "tipsc tips and corrections for programming moocs"
    - "automated correction for syntax errors in programming assignments using recurrent neural networks"
    - "learning program embeddings to propagate feedback on student code"
    - "feedback generation for performance problems in introductory programming assignments"

Connected through hyperedge HE-2 (induced by source paper "zero shot learning for code education rubric sampling with deep learning inference"):
  with members:
    - "multimodal generative models for scalable weakly supervised learning"
    - "zero shot learning via class conditioned deep generative models"
    - "multi label zero shot learning with structured knowledge graphs"
    - "generative models of visually grounded imagination"
    - "feature generating networks for zero shot learning"
    - "learning program embeddings to propagate feedback on student code"
    - "generating sentences from a continuous space"

Connected through hyperedge HE-3 (induced by source paper "tree edit distance learning via adaptive symbol embeddings"):
  with members:
    - "revisiting the tree edit distance and its backtracing a tutorial"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-4 (induced by source paper "revisiting the tree edit distance and its backtracing a tutorial"):
  with members:
    - "tree edit distance learning via adaptive symbol embeddings"
    - "rted a robust algorithm for the tree edit distance"

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)