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: "practical study of deterministic regular expressions from large scale xml and schema data"
  Abstract: Regular expressions are a fundamental concept in computer science and widely used in various applications. In this paper we focused on deterministic regular expressions (DREs). Considering that researchers didn't have large datasets as evidence before, we first harvested a large corpus of real data from the Web then conducted a practical study to investigate the usage of DREs. One feature of our work is that the data set is sufficiently large compared with previous work, which is obtained using several data collection strategies we proposed. The results show more than 98\% of expressions in Relax NG are DRE, and more than 56\% of expressions from RegExLib are DRE, while both Relax NG and RegExLib do not have the determinism constraint. These observations indicate that DREs are commonly used in practice. The results also show further study of subclasses of DREs is necessary. As far as we know, we are the first to analyze the determinism and the subclasses of DREs of Relax NG and RegExLib, and give these results. Furthermore, we give some discussions and applications of the data set. We obtain a DRE data set from the original data, which will be useful in practice and it has value in its own right. We find current research in new subclasses of DREs is insufficient, therefore it is necessary to do further study. We also analyze the referencing relationships among XSDs and define SchemaRank, which can be used in XML Schema design.

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

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)