human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning and designing stochastic processes from logical constraints"):
  Abstract: Stochastic processes offer a flexible mathematical formalism to model and#N#reason about systems. Most analysis tools, however, start from the premises#N#that models are fully specified, so that any parameters controlling the#N#system's dynamics must be known exactly. As this is seldom the case, many#N#methods have been devised over the last decade to infer (learn) such parameters#N#from observations of the state of the system. In this paper, we depart from#N#this approach by assuming that our observations are {\it qualitative}#N#properties encoded as satisfaction of linear temporal logic formulae, as#N#opposed to quantitative observations of the state of the system. An important#N#feature of this approach is that it unifies naturally the system identification#N#and the system design problems, where the properties, instead of observations,#N#represent requirements to be satisfied. We develop a principled statistical#N#estimation procedure based on maximising the likelihood of the system's#N#parameters, using recent ideas from statistical machine learning. We#N#demonstrate the efficacy and broad applicability of our method on a range of#N#simple but non-trivial examples, including rumour spreading in social networks#N#and hybrid models of gene regulation.

Includes members:
  Member "smoothed model checking for uncertain continuous time markov chains":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "parameter synthesis for markov models")
  Member "on the robustness of temporal properties for stochastic models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "falsification of cyber physical systems using deep reinforcement learning")
  Member "information theoretic regret bounds for gaussian process optimization in the bandit setting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits")

Question: Please tell me which class the source paper 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.SY(Systems and Control)