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: "validation and inference of schema level workflow data dependency annotations"
  Abstract: An advantage of scientific workflow systems is their ability to collect runtime provenance information as an execution trace. Traces include the computation steps invoked as part of the workflow run along with the corresponding data consumed and produced by each workflow step. The information captured by a trace is used to infer "lineage" relationships among data items, which can help answer provenance queries to find workflow inputs that were involved in producing specific workflow outputs. Determining lineage relationships, however, requires an understanding of the dependency patterns that exist between each workflow step's inputs and outputs, and this information is often under-specified or generally assumed by workflow systems. For instance, most approaches assume all outputs depend on all inputs, which can lead to lineage "false positives". In prior work, we defined annotations for specifying detailed dependency relationships between inputs and outputs of computation steps. These annotations are used to define corresponding rules for inferring fine-grained data dependencies from a trace. In this paper, we extend our previous work by considering the impact of dependency annotations on workflow specifications. In particular, we provide a reasoning framework to ensure the set of dependency annotations on a workflow specification is consistent. The framework can also infer a complete set of annotations given a partially annotated workflow. Finally, we describe an implementation of the reasoning framework using answer-set programming.

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)