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: "learning deterministic regular expressions for the inference of schemas from xml data"
  Abstract: Inferring an appropriate DTD or XML Schema Definition (XSD) for a given collection of XML documents essentially reduces to learning deterministic regular expressions from sets of positive example words. Unfortunately, there is no algorithm capable of learning the complete class of deterministic regular expressions from positive examples only, as we will show. The regular expressions occurring in practical DTDs and XSDs, however, are such that every alphabet symbol occurs only a small number of times. As such, in practice it suffices to learn the subclass of deterministic regular expressions in which each alphabet symbol occurs at most k times, for some small k. We refer to such expressions as k-occurrence regular expressions (k-OREs for short). Motivated by this observation, we provide a probabilistic algorithm that learns k-OREs for increasing values of k, and selects the deterministic one that best describes the sample based on a Minimum Description Length argument. The effectiveness of the method is empirically validated both on real world and synthetic data. Furthermore, the method is shown to be conservative over the simpler classes of expressions considered in previous work.

Connected through hyperedge HE-1 (induced by source paper "an effective algorithm for learning single occurrence regular expressions with interleaving"):
  with members:
    - "practical study of deterministic regular expressions from large scale xml and schema data"
    - "discovering restricted regular expressions with interleaving"
    - "simple schemas for unordered xml"
    - "learning schemas for unordered xml"

Connected through hyperedge HE-2 (induced by source paper "deterministic regular expressions with back references"):
  with members:
    - "more than 1700 years of word equations"

Connected through hyperedge HE-3 (induced by source paper "an incremental learner for language based anomaly detection in xml"):
  with members:
    - "a grammatical inference approach to language based anomaly detection in xml"

Connected through hyperedge HE-4 (induced by source paper "discovering restricted regular expressions with interleaving"):
  with members:
    - "schemas for unordered xml on a dime"
    - "simple schemas for unordered xml"
    - "learning schemas for unordered xml"

Connected through hyperedge HE-5 (induced by source paper "sculpt a schema language for tabular data on the web"):
  with members:
    - "semantics and complexity of sparql"

Connected through hyperedge HE-6 (induced by source paper "learning schemas for unordered xml"):
  with members:
    - "simple schemas for unordered xml"

Connected through hyperedge HE-7 (induced by source paper "learning xml twig queries"):
  with members:
    - "logics for unranked trees an overview"

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.DB(Databases)