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: "in the field monitoring of interactive applications"
  Abstract: Monitoring techniques can extract accurate data about the behavior of software systems. When used in the field, they can reveal how applications behave in real-world contexts and how programs are actually exercised by their users. Nevertheless, since monitoring might need significant storage and computational resources, it may interfere with users activities degrading the quality of the user experience. While the impact of monitoring has been typically studied by measuring the overhead that it may introduce in a monitored application, there is little knowledge about how monitoring solutions may actually impact on the user experience and to what extent users may recognize their presence. In this paper, we present our investigation on how collecting data in the field may impact the quality of the user experience. Our initial results show that non-trivial overhead can be tolerated by users, depending on the kind of activity that is performed. This opens interesting opportunities for research in monitoring solutions, which could be designed to opportunistically

Connected through hyperedge HE-1 (induced by source paper "cbr controlled burst recording"):
  with members:
    - "timed k tail automatic inference of timed automata"
    - "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:
    - "timed k tail automatic inference of timed automata"
    - "flexible in the field monitoring"

Connected through hyperedge HE-3 (induced by source paper "controlling interactions with libraries in android apps through runtime enforcement"):
  with members:
    - "a benchmark of data loss bugs for android apps"
    - "increasing the reusability of enforcers with lifecycle events"
    - "verifying policy enforcers"
    - "policy enforcement with proactive libraries"
    - "healing data loss problems in android apps"

Connected through hyperedge HE-4 (induced by source paper "flexible in the field monitoring"):
  with members:
    - "a platform for automating chaos experiments"

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)