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: "natural models for evolution on networks"
  Abstract: Evolutionary dynamics have been traditionally studied in the context of homogeneous populations, mainly described my the Moran process. Recently, this approach has been generalized in \cite{LHN} by arranging individuals on the nodes of a network. Undirected networks seem to have a smoother behavior than directed ones, and thus it is more challenging to find suppressors/amplifiers of selection. In this paper we present the first class of undirected graphs which act as suppressors of selection, by achieving a fixation probability that is at most one half of that of the complete graph, as the number of vertices increases. Moreover, we provide some generic upper and lower bounds for the fixation probability of general undirected graphs. As our main contribution, we introduce the natural alternative of the model proposed in \cite{LHN}, where all individuals interact simultaneously and the result is a compromise between aggressive and non-aggressive individuals. That is, the behavior of the individuals in our new model and in the model of \cite{LHN} can be interpreted as an "aggregation" vs. an "all-or-nothing" strategy, respectively. We prove that our new model of mutual influences admits a \emph{potential function}, which guarantees the convergence of the system for any graph topology and any initial fitness vector of the individuals. Furthermore, we prove fast convergence to the stable state for the case of the complete graph, as well as we provide almost tight bounds on the limit fitness of the individuals. Apart from being important on its own, this new evolutionary model appears to be useful also in the abstract modeling of control mechanisms over invading populations in networks. We demonstrate this by introducing and analyzing two alternative control approaches, for which we bound the time needed to stabilize to the "healthy" state of the system.

Connected through hyperedge HE-1 (induced by source paper "strong amplifiers of natural selection proofs"):
  with members:
    - "faster monte carlo algorithms for fixation probability of the moran process on undirected graphs"
    - "amplifiers and suppressors of selection for the moran process on undirected graphs"
    - "absorption time of the moran process"

Connected through hyperedge HE-2 (induced by source paper "amplifiers and suppressors of selection for the moran process on undirected graphs"):
  with members:
    - "absorption time of the moran process"

Connected through hyperedge HE-3 (induced by source paper "breathe before speaking efficient information dissemination despite noisy limited and anonymous communication"):
  with members:
    - "the power of two choices in distributed voting"
    - "optimal gossip with direct addressing"
    - "simple dynamics for plurality consensus"
    - "memory lower bounds for randomized collaborative search and applications to biology"
    - "collaborative search on the plane without communication"
    - "beeping a maximal independent set"
    - "simple fast and deterministic gossip and rumor spreading"
    - "global computation in a poorly connected world fast rumor spreading with no dependence on conductance"

Connected through hyperedge HE-4 (induced by source paper "absorption time of the moran process"):
  with members:
    - "on the fixation probability of superstars"
    - "approximating fixation probabilities in the generalized moran process"

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)