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: "generating connected acyclic digraphs uniformly at random"
  Abstract: We describe a simple algorithm based on a Markov chain process to generate simply connected acyclic directed graphs over a fixed set of vertices. This algorithm is an extension of a previous one, designed to generate acyclic digraphs, non necessarily connected.

Connected through hyperedge HE-1 (induced by source paper "learning bayesian networks from demographic and health survey data"):
  with members:
    - "evaluating structure learning algorithms with a balanced scoring function"
    - "exposing the probabilistic causal structure of discrimination"
    - "d separation from theorems to algorithms"

Connected through hyperedge HE-2 (induced by source paper "a comparison of random task graph generation methods for scheduling problems"):
  with members:
    - "a markov chain monte carlo approach to cost matrix generation for scheduling performance evaluation"
    - "experimental algorithm for the maximum independent set problem"

Connected through hyperedge HE-3 (induced by source paper "genetic algorithm based resource minimization in network code based peer to peer network"):
  with members:
    - "evolutionary approaches to minimizing network coding resources"
    - "minimal network coding for multicast"

Connected through hyperedge HE-4 (induced by source paper "combinatorial decomposition approaches for efficient counting and random generation fptases"):
  with members:
    - "counting approximately shortest paths in directed acyclic graphs"
    - "a deterministic polynomial time approximation scheme for counting knapsack solutions"
    - "polynomial time approximation schemes for knapsack and related counting problems using branching programs"

Connected through hyperedge HE-5 (induced by source paper "evolutionary approaches to minimizing network coding resources"):
  with members:
    - "genetic representations for evolutionary minimization of network coding resources"
    - "minimal network coding for multicast"

Connected through hyperedge HE-6 (induced by source paper "genetic representations for evolutionary minimization of network coding resources"):
  with members:
    - "evolutionary approaches to minimizing network coding resources"

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)