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: "average distance in a general class of scale free networks with underlying geometry"
  Abstract: In Chung-Lu random graphs, a classic model for real-world networks, each vertex is equipped with a weight drawn from a power-law distribution, and two vertices form an edge independently with probability proportional to the product of their weights. Chung-Lu graphs have average distance O(log log n) and thus reproduce the small-world phenomenon, a key property of real-world networks. Modern, more realistic variants of this model also equip each vertex with a random position in a specific underlying geometry. The edge probability of two vertices then depends, say, inversely polynomial on their distance. #R##N#In this paper we study a generic augmented version of Chung-Lu random graphs. We analyze a model where the edge probability of two vertices can depend arbitrarily on their positions, as long as the marginal probability of forming an edge (for two vertices with fixed weights, one fixed position, and one random position) is as in Chung-Lu random graphs. The resulting class contains Chung-Lu random graphs, hyperbolic random graphs, and geometric inhomogeneous random graphs as special cases. #R##N#Our main result is that every random graph model in this general class has the same average distance as Chung-Lu random graphs, up to a factor 1+o(1). This shows in particular that specific choices, such as the underlying geometry being Euclidean or the dependence on the distance being inversely polynomial, do not significantly influence the average distance. The proof also yields that our model has a giant component and polylogarithmic diameter with high probability.

Connected through hyperedge HE-1 (induced by source paper "greedy routing and the algorithmic small world phenomenom"):
  with members:
    - "an experimental investigation of hyperbolic routing with a smart forwarding plane in ndn"
    - "network mapping by replaying hyperbolic growth"
    - "geometric inhomogeneous random graphs"
    - "on the diameter of hyperbolic random graphs"
    - "four degrees of separation"
    - "sustaining the internet with hyperbolic mapping"
    - "networks become navigable as nodes move and forget"
    - "on compact routing for the internet"

Connected through hyperedge HE-2 (induced by source paper "rumours spread slowly in a small world spatial network"):
  with members:
    - "non uniform distribution of nodes in the spatial preferential attachment model"
    - "dimensionality of social networks using motifs and eigenvalues"
    - "geometric graph properties of the spatial preferred attachment model"

Connected through hyperedge HE-3 (induced by source paper "geometric inhomogeneous random graphs"):
  with members:
    - "greedy routing and the algorithmic small world phenomenom"
    - "network mapping by replaying hyperbolic growth"
    - "fast generation of dynamic complex networks with underlying hyperbolic geometry"
    - "on the diameter of hyperbolic random graphs"
    - "fast generation of complex networks with underlying hyperbolic geometry"
    - "sustaining the internet with hyperbolic mapping"

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)