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: "the degree distribution of random k trees"
  Abstract: A power law degree distribution is established for a graph evolution model based on the graph class of k-trees. This k-tree-based graph process can be viewed as an idealized model that captures some characteristics of the preferential attachment and copying mechanisms that existing evolving graph processes fail to model due to technical obstacles. The result also serves as a further cautionary note reinforcing the point of view that a power law degree distribution should not be regarded as the only important characteristic of a complex network, as has been previously argued [D. Achlioptas, A. Clauset, D. Kempe, C. Moore, On the bias of traceroute sampling, or power-law degree distribution in regular graphs, in: Proceedings of the 37th ACM Symposium on Theory of Computing, STOC'05, 2005, pp. 694-703; L. Li, D. Alderson, J. Doyle, W. Willinger, Towards a theory of scale-free graphs: Definition, properties, and implications, Internet Mathematics 2 (4) (2005) 431-523; M. Mitzenmacher, The future of power law research, Internet Mathematics, 2 (4) (2005) 525-534].

Connected through hyperedge HE-1 (induced by source paper "scalable betweenness centrality maximization via sampling"):
  with members:
    - "tri est counting local and global triangles in fully dynamic streams with fixed memory size"
    - "tree decompositions and social graphs"
    - "a divide and conquer algorithm for betweenness centrality"
    - "triadic measures on graphs the power of wedge sampling"
    - "axioms for centrality"
    - "maximizing social influence in nearly optimal time"
    - "shattering and compressing networks for centrality analysis"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"

Connected through hyperedge HE-2 (induced by source paper "every property is testable on a natural class of scale free multigraphs"):
  with members:
    - "a quasi polynomial time partition oracle for graphs with an excluded minor"

Connected through hyperedge HE-3 (induced by source paper "justifying the small world phenomenon via random recursive trees"):
  with members:
    - "rumours spread slowly in a small world spatial network"
    - "it s a small world for random surfers"
    - "four degrees of separation"

Connected through hyperedge HE-4 (induced by source paper "randomized rumor spreading in poorly connected small world networks"):
  with members:
    - "tight bounds for rumor spreading with vertex expansion"
    - "global computation in a poorly connected world fast rumor spreading with no dependence on conductance"

Connected through hyperedge HE-5 (induced by source paper "generalized belief propagation on tree robust structured region graphs"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"
    - "sufficient conditions for convergence of loopy belief propagation"
    - "on the choice of regions for generalized belief propagation"
    - "convergent message passing algorithms for inference over general graphs with convex free energies"
    - "iterative join graph propagation"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "structured region graphs morphing ep into gbp"

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)