human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "geometric inhomogeneous random graphs"):
  Abstract: Real-world networks, like social networks or the internet infrastructure, have structural properties such as their large clustering coefficient that can best be described in terms of an underlying geometry. This is why the focus of the literature on theoretical models for real-world networks shifted from classic models without geometry, such as Chung-Lu random graphs, to modern geometry-based models, such as hyperbolic random graphs. #R##N#With this paper we contribute to the theoretical analysis of these modern, more realistic random graph models. However, we do not directly study hyperbolic random graphs, but replace them by a more general model that we call \emph{geometric inhomogeneous random graphs} (GIRGs). Since we ignore constant factors in the edge probabilities, our model is technically simpler (specifically, we avoid hyperbolic cosines), while preserving the qualitative behaviour of hyperbolic random graphs, and we suggest to replace hyperbolic random graphs by our new model in future theoretical studies. #R##N#We prove the following fundamental structural and algorithmic results on GIRGs. (1) We provide a sampling algorithm that generates a random graph from our model in expected linear time, improving the best-known sampling algorithm for hyperbolic random graphs by a factor $O(\sqrt{n})$, (2) we establish that GIRGs have a constant clustering coefficient, (3) we show that GIRGs have small separators, i.e., it suffices to delete a sublinear number of edges to break the giant component into two large pieces, and (4) we show how to compress GIRGs using an expected linear number of bits.

Includes members:
  Member "average distance in a general class of scale free networks with underlying geometry":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "greedy routing and the algorithmic small world phenomenom")
  Member "greedy routing and the algorithmic small world phenomenom":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "geohyperbolic routing and addressing schemes")
  Member "network mapping by replaying hyperbolic growth":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "social centralization and semantic collapse hyperbolic embeddings of networks and text")
  Member "fast generation of dynamic complex networks with underlying hyperbolic geometry":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "engineering parallel algorithms for community detection in massive networks")
  Member "on the diameter of hyperbolic random graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "recent advances in scalable network generation")
  Member "fast generation of complex networks with underlying hyperbolic geometry":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "updating dynamic random hyperbolic graphs in sublinear time")
  Member "sustaining the internet with hyperbolic mapping":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hyperkg hyperbolic knowledge graph embeddings for knowledge base completion")

Question: Please tell me which class the source paper 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.SI(Social and Information Networks)