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: "compositionality of approximate bisimulation for probabilistic systems"
  Abstract: Probabilistic transition system specifications using the rule format ntmuft-ntmuxt provide structural operational semantics for Segala-type systems and guarantee that probabilistic bisimilarity is a congruence. Probabilistic bisimilarity is for many applications too sensitive to the exact probabilities of transitions. Approximate bisimulation provides a robust semantics that is stable with respect to implementation and measurement errors of probabilistic behavior. We provide a general method to quantify how much a process combinator expands the approximate bisimulation distance. As a direct application we derive an appropriate rule format that guarantees compositionality with respect to approximate bisimilarity. Moreover, we describe how specification formats for non-standard compositionality requirements may be derived.

Connected through hyperedge HE-1 (induced by source paper "equational reasonings in wireless network gossip protocols"):
  with members:
    - "a probabilistic calculus of cyber physical systems"
    - "modelling mac layer communications in wireless systems"
    - "a process algebra for wireless mesh networks"
    - "fixed point characterization of compositionality properties of probabilistic processes combinators"
    - "an analytical model of information dissemination for a gossip based protocol"

Connected through hyperedge HE-2 (induced by source paper "lean and full congruence formats for recursion"):
  with members:
    - "gsos for non deterministic processes with quantitative aspects"
    - "musings on encodings and expressiveness"
    - "branching bisimilarity with explicit divergence"

Connected through hyperedge HE-3 (induced by source paper "compositional bisimulation metric reasoning with probabilistic process calculi"):
  with members:
    - "logical characterization of bisimulation metrics"
    - "fixed point characterization of compositionality properties of probabilistic processes combinators"
    - "tree rules in probabilistic transition system specifications with negative and quantitative premises"

Connected through hyperedge HE-4 (induced by source paper "towards trace metrics via functor lifting"):
  with members:
    - "behavioral metrics via functor lifting"
    - "coinduction up to in a fibrational setting"
    - "generalizing determinization from automata to coalgebras"
    - "generic trace semantics via coinduction"

Connected through hyperedge HE-5 (induced by source paper "behavioral metrics via functor lifting"):
  with members:
    - "coinduction up to in a fibrational setting"
    - "approximating a behavioural pseudometric without discount for probabilistic systems"
    - "generic trace semantics via coinduction"

Connected through hyperedge HE-6 (induced by source paper "fixed point characterization of compositionality properties of probabilistic processes combinators"):
  with members:
    - "tree rules in probabilistic transition system specifications with negative and quantitative premises"
    - "computing distances between probabilistic automata"
    - "congruence from the operator s point of view compositionality requirements on process semantics"

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)