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: "semantics and algorithms for parametric monitoring"
  Abstract: Analysis of execution traces plays a fundamental role in many program anal- ysis approaches, such as runtime verication, testing, monitoring, and specication min- ing. Execution traces are frequently parametric, i.e., they contain events with param- eter bindings. Each parametric trace usually consists of many meaningful trace slices merged together, each slice corresponding to one parameter binding. For example, a Java program creating iterator objects i1 and i2 over collection object c1 may yield a trace createIterhc1i1i nexthi1i createIterhc1i2i updateCollhc1i nexthi1i parametric in collection c and iterator i, whose slices corresponding to instances \c;i 7! c1;i1" and \c;i 7! c1;i2" are createIterhc1i1i nexthi1i updateCollhc1i nexthi1i and, respectively, createIterhc1i2i updateCollhc1i. Several approaches have been proposed to specify and dynamically analyze parametric properties, but they have limitations: some in the specication

Connected through hyperedge HE-1 (induced by source paper "runtime verification over out of order streams"):
  with members:
    - "runtime verification of temporal properties over out of order data streams"
    - "efficient and generalized decentralized monitoring of regular languages"
    - "decentralised ltl monitoring"
    - "ltl with the freeze quantifier and register automata"

Connected through hyperedge HE-2 (induced by source paper "four valued monitorability of omega regular languages"):
  with members:
    - "adventures in monitorability from branching to linear time and back again"
    - "an operational guide to monitorability"

Connected through hyperedge HE-3 (induced by source paper "efficient large scale trace checking using mapreduce"):
  with members:
    - "trace checking of metric temporal logic with aggregating modalities using mapreduce"
    - "decentralised ltl monitoring"

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.PL(Programming Languages)