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: "absorption time of the moran process"
  Abstract: The Moran process models the spread of mutations in populations on graphs. We investigate the absorption time of the process, which is the time taken for a mutation introduced at a randomly chosen vertex to either spread to the whole population, or to become extinct. It is known that the expected absorption time for an advantageous mutation is O(n^4) on an n-vertex undirected graph, which allows the behaviour of the process on undirected graphs to be analysed using the Markov chain Monte Carlo method. We show that this does not extend to directed graphs by exhibiting an infinite family of directed graphs for which the expected absorption time is exponential in the number of vertices. However, for regular directed graphs, we show that the expected absorption time is Omega(n log n) and O(n^2). We exhibit families of graphs matching these bounds and give improved bounds for other families of graphs, based on isoperimetric number. Our results are obtained via stochastic dominations which we demonstrate by establishing a coupling in a related continuous-time model. The coupling also implies several natural domination results regarding the fixation probability of the original (discrete-time) process, resolving a conjecture of Shakarian, Roos and Johnson.

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"
    - "natural models for evolution on networks"

Connected through hyperedge HE-2 (induced by source paper "ignore or comply on breaking symmetry in consensus"):
  with members:
    - "bounds on the voter model in dynamic networks"
    - "efficient k party voting with two choices"
    - "stabilizing consensus with many opinions"
    - "the power of two choices in distributed voting"
    - "plurality consensus in the gossip model"
    - "simple dynamics for plurality consensus"
    - "coalescing random walks and voting on connected graphs"

Connected through hyperedge HE-3 (induced by source paper "bounds on the voter model in dynamic networks"):
  with members:
    - "global majority consensus by local majority polling on graphs of a given degree sequence"
    - "efficient k party voting with two choices"
    - "the power of two choices in distributed voting"
    - "coalescing random walks and voting on connected graphs"
    - "approximating fixation probabilities in the generalized moran process"

Connected through hyperedge HE-4 (induced by source paper "amplifiers and suppressors of selection for the moran process on undirected graphs"):
  with members:
    - "natural models for evolution on 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)