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 schemas for unordered xml"
  Abstract: We consider unordered XML, where the relative order among siblings is ignored, and we investigate the problem of learning schemas from examples given by the user. We focus on the schema formalisms proposed in [10]: disjunctive multiplicity schemas (DMS) and its restriction, disjunction-free multiplicity schemas (MS). A learning algorithm takes as input a set of XML documents which must satisfy the schema (i.e., positive examples) and a set of XML documents which must not satisfy the schema (i.e., negative examples), and returns a schema consistent with the examples. We investigate a learning framework inspired by Gold [18], where a learning algorithm should be sound i.e., always return a schema consistent with the examples given by the user, and complete i.e., able to produce every schema with a sufficiently rich set of examples. Additionally, the algorithm should be efficient i.e., polynomial in the size of the input. We prove that the DMS are learnable from positive examples only, but they are not learnable when we also allow negative examples. Moreover, we show that the MS are learnable in the presence of positive examples only, and also in the presence of both positive and negative examples. Furthermore, for the learnable cases, the proposed learning algorithms return minimal schemas consistent with the examples.

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 deterministic regular expressions for the inference of schemas from xml data"

Connected through hyperedge HE-2 (induced by source paper "learning restricted regular expressions with interleaving"):
  with members:
    - "discovering restricted regular expressions with interleaving"
    - "simple schemas for unordered xml"
    - "schema based scheduling of event processors and buffer minimization for queries on structured data streams"

Connected through hyperedge HE-3 (induced by source paper "schemas for unordered xml on a dime"):
  with members:
    - "validating rdf with shape expressions"
    - "simple schemas for unordered xml"
    - "on the complexity of xpath containment in the presence of disjunction dtds and variables"

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 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)