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: "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.

Connected through hyperedge HE-1 (induced by source paper "recent advances in scalable network generation"):
  with members:
    - "efficiently generating geometric inhomogeneous and hyperbolic random graphs"
    - "exploring randomly wired neural networks for image recognition"
    - "parallel weighted random sampling"
    - "linear work generation of r mat graphs"
    - "egbter capturing degree distribution clustering coefficients and community structure in a single random graph model"
    - "multiscale planar graph generation"
    - "parallel and i o efficient randomisation of massive networks using global curveball trades"
    - "efficient parallel random sampling vectorized cache efficient and online"

Connected through hyperedge HE-2 (induced by source paper "updating dynamic random hyperbolic graphs in sublinear time"):
  with members:
    - "nearest neighbor searching under uncertainty ii"
    - "querying probabilistic neighborhoods in spatial data sets efficiently"
    - "generating massive complex networks with hyperbolic geometry faster in practice"
    - "generating scaled replicas of real world complex networks"
    - "sampling geometric inhomogeneous random graphs in linear time"
    - "approximating betweenness centrality in fully dynamic networks"
    - "on the diameter of hyperbolic random graphs"
    - "fast generation of complex networks with underlying hyperbolic geometry"

Connected through hyperedge HE-3 (induced by source paper "generating connected random graphs"):
  with members:
    - "super blockers and the effect of network structure on information cascades"
    - "the flip markov chain for connected regular graphs"
    - "generating constrained random graphs using multiple edge switches"

Connected through hyperedge HE-4 (induced by source paper "geohyperbolic routing and addressing schemes"):
  with members:
    - "greedy routing and the algorithmic small world phenomenom"
    - "generating massive complex networks with hyperbolic geometry faster in practice"
    - "an experimental investigation of hyperbolic routing with a smart forwarding plane in ndn"
    - "sampling geometric inhomogeneous random graphs in linear time"
    - "network mapping by replaying hyperbolic growth"
    - "network geometry inference using common neighbors"
    - "sustaining the internet with hyperbolic mapping"

Connected through hyperedge HE-5 (induced by source paper "communication free massively distributed graph generation"):
  with members:
    - "efficient parallel random sampling vectorized cache efficient and online"
    - "generating massive complex networks with hyperbolic geometry faster in practice"
    - "scalable generation of scale free graphs"
    - "sampling geometric inhomogeneous random graphs in linear time"
    - "engineering a scalable high quality graph partitioner"

Connected through hyperedge HE-6 (induced by source paper "hyperbolic triangulations and discrete random graphs"):
  with members:
    - "bounded degree and planar spectra"
    - "network geometry inference using common neighbors"

Connected through hyperedge HE-7 (induced by source paper "bounds on the satisfiability threshold for power law distributed random sat"):
  with members:
    - "the fractal dimension of sat formulas"

Connected through hyperedge HE-8 (induced by source paper "greedy routing and the algorithmic small world phenomenom"):
  with members:
    - "average distance in a general class of scale free networks with underlying geometry"
    - "an experimental investigation of hyperbolic routing with a smart forwarding plane in ndn"
    - "network mapping by replaying hyperbolic growth"
    - "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"

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.SI(Social and Information Networks)