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: "timed k tail automatic inference of timed automata"
  Abstract: Accurate and up-to-date models describing the be- havior of software systems are seldom available in practice. To address this issue, software engineers may use specification mining techniques, which can automatically derive models that capture the behavior of the system under analysis. So far, most specification mining techniques focused on the functional behavior of the systems, with specific emphasis on models that represent the ordering of operations, such as tempo- ral rules and finite state models. Although useful, these models are inherently partial. For instance, they miss the timing behavior, which is extremely relevant for many classes of systems and com- ponents, such as shared libraries and user-driven applications. Mining specifications that include both the functional and the timing aspects can improve the applicability of many testing and analysis solutions. This paper addresses this challenge by presenting the Timed k-Tail (TkT) specification mining technique that can mine timed automata from program traces. Since timed automata can effectively represent the interplay between the functional and the timing behavior of a system, TkT could be exploited in those contexts where time-related information is relevant. Our empirical evaluation shows that TkT can efficiently and effectively mine accurate models. The mined models have been used to identify executions with anomalous timing. The evaluation shows that most of the anomalous executions have been correctly identified while producing few false positives.

Connected through hyperedge HE-1 (induced by source paper "cbr controlled burst recording"):
  with members:
    - "in the field monitoring of interactive applications"
    - "a survey of symbolic execution techniques"
    - "a platform for automating chaos experiments"

Connected through hyperedge HE-2 (induced by source paper "in the field monitoring of functional calls is it feasible"):
  with members:
    - "in the field monitoring of interactive applications"
    - "flexible in the field monitoring"

Connected through hyperedge HE-3 (induced by source paper "learning timed automata via genetic programming"):
  with members:
    - "uncertainty driven black box test data generation"
    - "equivalence of probabilistic tournament and polynomial ranking selection"

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)