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: "on the conversion of multivalued gene regulatory networks to boolean dynamics"
  Abstract: We consider the modeling approach introduced by R. Thomas for the qualitative study of gene regulatory networks. Tools and results on regulatory networks are often concerned only with the Boolean case of this formalism. However, multivalued approaches are sometimes more suited to model biological situations. Multivalued networks can be converted to partial Boolean maps, in a way that preserves the asynchronous dynamics. We ask whether this map can provide information on the original multivalued function, in particular via the application of Thomas' rules. The problem of extending these partial Boolean maps to non-admissible states, i.e. states that do not have a multivalued counterpart, is also investigated. We observe that attractors are preserved if a "unitary" version of the original function is considered for conversion. Different extensions of the Boolean counterpart affect the structure of the regulatory graph in different ways. A particular technique for extending the Boolean unitary version of the network is identified, that ensures that no new circuits are added. This property, combined with the preservation of the asymptotic behaviour, can prove useful for the application of results and analyses defined in the Boolean setting to multivalued networks, and vice versa. By considering the conversion of a known example for the discrete multivalued case, we create a Boolean map showing that, for $n \geq 6$, the absence of fixed points is compatible with the absence of local negative circuits in the regulatory graph.

Connected through hyperedge HE-1 (induced by source paper "local negative circuits and cyclic attractors in boolean networks with at most five components"):
  with members:
    - "negative circuits and sustained oscillations in asynchronous automata networks"

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.DM(Discrete Mathematics)