human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning xml twig queries"):
  Abstract: We investigate the problem of learning XML queries, path queries and tree pattern queries, from examples given by the user. A learning algorithm takes on the input a set of XML documents with nodes annotated by the user and returns a query that selects the nodes in a manner consistent with the annotation. We study two learning settings that differ with the types of annotations. In the first setting the user may only indicate required nodes that the query must return. In the second, more general, setting, the user may also indicate forbidden nodes that the query must not return. The query may or may not return any node with no annotation. We formalize what it means for a class of queries to be \emph{learnable}. One requirement is the existence of a learning algorithm that is sound i.e., always returns a query consistent with the examples given by the user. Furthermore, the learning algorithm should be complete i.e., able to produce every query with a sufficiently rich example. Other requirements involve tractability of learning and its robustness to nonessential examples. We show that the classes of simple path queries and path-subsumption-free tree queries are learnable from positive examples. The learnability of the full class of tree pattern queries (and the full class of path queries) remains an open question. We show also that adding negative examples to the picture renders the learning unfeasible. #R##N#Published in ICDT 2012, Berlin.

Includes members:
  Member "learning deterministic regular expressions for the inference of schemas from xml data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an effective algorithm for learning single occurrence regular expressions with interleaving")
  Member "logics for unranked trees an overview":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "css minification via constraint solving technical report")

Question: Please tell me which class the source paper 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.DB(Databases)