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: "lumpability abstractions of rule based systems"
  Abstract: The induction of a signaling pathway is characterized by transient complex formation and mutual posttranslational modification of proteins. To faithfully capture this combinatorial process in a mathematical model is an important challenge in systems biology. Exploiting the limited context on which most binding and modification events are conditioned, attempts have been made to reduce the combinatorial complexity by quotienting the reachable set of molecular species, into species aggregates while preserving the deterministic semantics of the thermodynamic limit. Recently we proposed a quotienting that also preserves the stochastic semantics and that is complete in the sense that the semantics of individual species can be recovered from the aggregate semantics. In this paper we prove that this quotienting yields a sufficient condition for weak lumpability and that it gives rise to a backward Markov bisimulation between the original and aggregated transition system. We illustrate the framework on a case study of the EGF/insulin receptor crosstalk.

Connected through hyperedge HE-1 (induced by source paper "combinatorial conversion and moment bisimulation for stochastic rewriting systems"):
  with members:
    - "rule algebras for adhesive categories"
    - "forward and backward bisimulations for chemical reaction networks"

Connected through hyperedge HE-2 (induced by source paper "markov chain aggregation and its application to rule based modelling"):
  with members:
    - "markov chain aggregation and its applications to combinatorial reaction networks"

Connected through hyperedge HE-3 (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"
    - "approximate reasoning for real time probabilistic processes"

Connected through hyperedge HE-4 (induced by source paper "syntactic markovian bisimulation for chemical reaction networks"):
  with members:
    - "forward and backward bisimulations for chemical reaction networks"

Connected through hyperedge HE-5 (induced by source paper "challenges in quantitative abstractions for collective adaptive systems"):
  with members:
    - "forward and backward bisimulations for chemical reaction networks"

Connected through hyperedge HE-6 (induced by source paper "optimal kullback leibler aggregation via information bottleneck"):
  with members:
    - "lumpings of markov chains entropy rate preservation and higher order lumpability"
    - "mean field variational approximation for continuous time bayesian networks"
    - "signal enhancement as minimization of relevant information loss"
    - "a metric between probability distributions on finite sets of different cardinalities and applications to order reduction"

Connected through hyperedge HE-7 (induced by source paper "efficient reduction of kappa models by static inspection of the rule set"):
  with members:
    - "a temporal logic approach to modular design of synthetic biological circuits"
    - "markov chain aggregation and its applications to combinatorial reaction networks"

Connected through hyperedge HE-8 (induced by source paper "forward and backward bisimulations for chemical reaction networks"):
  with members:
    - "verifying chemical reaction network implementations a pathway decomposition approach"

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.CE(Computational Engineering, Finance, and Science)