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: "approximate reasoning for real time probabilistic processes"
  Abstract: We develop a pseudo-metric analogue of bisimulation for generalized#N#semi-Markov processes. The kernel of this pseudo-metric corresponds to#N#bisimulation; thus we have extended bisimulation for continuous-time#N#probabilistic processes to a much broader class of distributions than#N#exponential distributions. This pseudo-metric gives a useful handle on#N#approximate reasoning in the presence of numerical information -- such as#N#probabilities and time -- in the model. We give a fixed point characterization#N#of the pseudo-metric. This makes available coinductive reasoning principles for#N#reasoning about distances. We demonstrate that our approach is insensitive to#N#potentially ad hoc articulations of distance by showing that it is intrinsic to#N#an underlying uniformity. We provide a logical characterization of this#N#uniformity using a real-valued modal logic. We show that several quantitative#N#properties of interest are continuous with respect to the pseudo-metric. Thus,#N#if two processes are metrically close, then observable quantitative properties#N#of interest are indeed close.

Connected through hyperedge HE-1 (induced by source paper "language based abstractions for dynamical systems"):
  with members:
    - "forward and backward bisimulations for chemical reaction networks"
    - "statistical model checking an overview"
    - "lumpability abstractions of rule based systems"

Connected through hyperedge HE-2 (induced by source paper "topologies of stochastic markov models computational aspects"):
  with members:
    - "approximating a behavioural pseudometric without discount for probabilistic systems"

Connected through hyperedge HE-3 (induced by source paper "coinductive proof principles for stochastic processes"):
  with members:
    - "applications of metric coinduction"

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.LO(Logic in Computer Science)