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: "on flexible web services composition networks"
  Abstract: The semantic Web service community develops efforts to bring semantics to Web service descriptions and allow automatic discovery and composition. However, there is no widespread adoption of such descriptions yet, because semantically defining Web services is highly complicated and costly. As a result, production Web services still rely on syntactic descriptions, key-word based discovery and predefined compositions. Hence, more advanced research on syntactic Web services is still ongoing. In this work we build syntactic composition Web services networks with three well known similarity metrics, namely Levenshtein, Jaro and Jaro-Winkler. We perform a comparative study on the metrics performance by studying the topological properties of networks built from a test collection of real-world descriptions. It appears Jaro-Winkler finds more appropriate similarities and can be used at higher thresholds. For lower thresholds, the Jaro metric would be preferable because it detect less irrelevant relationships.

Connected through hyperedge HE-1 (induced by source paper "analyzing web services networks a ws next application"):
  with members:
    - "topological properties of web services similarity networks"
    - "web services dependency networks analysis"
    - "mataws a multimodal approach for automatic ws semantic annotation"
    - "benefits of semantics on web service composition from a complex network perspective"

Connected through hyperedge HE-2 (induced by source paper "a comparative study of web services composition networks"):
  with members:
    - "web services dependency networks analysis"
    - "benefits of semantics on web service composition from a complex network perspective"

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.SE(Software Engineering)