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: "hyperbolicity degeneracy and expansion of random intersection graphs"
  Abstract: We establish the conditions under which several algorithmically exploitable structural features hold for random intersection graphs, a natural model for many real-world networks where edges correspond to shared attributes. Specifically, we fully characterize the degeneracy of random intersection graphs, and prove that the model asymptotically almost surely produces graphs with hyperbolicity at least $\log{n}$. Further, we prove that in the parametric regime where random intersection graphs are degenerate an even stronger notion of sparseness, so called bounded expansion, holds with high probability. We supplement our theoretical findings with experimental evaluations of the relevant statistics.

Connected through hyperedge HE-1 (induced by source paper "k connectivity in random key graphs with unreliable links"):
  with members:
    - "random intersection graphs and their applications in security wireless communication and social networks"
    - "connectivity in secure wireless sensor networks under transmission constraints"
    - "on the strengths of connectivity and robustness in general random intersection graphs"
    - "the phase transition in inhomogeneous random intersection graphs"
    - "k connectivity in secure wireless sensor networks with physical link constraints the on off channel model"
    - "performance of the eschenauer gligor key distribution scheme under an on off channel"

Connected through hyperedge HE-2 (induced by source paper "topological properties of secure wireless sensor networks under the q composite key predistribution scheme with unreliable links"):
  with members:
    - "k connectivity in random key graphs with unreliable links"
    - "on resilience and connectivity of secure wireless sensor networks under node capture attacks"
    - "key pre distributions from graph based block designs"
    - "robustness and algebraic connectivity of random interdependent networks"
    - "connectivity in secure wireless sensor networks under transmission constraints"
    - "on topological properties of wireless sensor networks under the q composite key predistribution scheme with on off channels"
    - "performance of the eschenauer gligor key distribution scheme under an on off channel"

Connected through hyperedge HE-3 (induced by source paper "asymptotic analysis of equivalences and core structures in kronecker style graph models"):
  with members:
    - "core periphery structure in networks"
    - "the similarity between stochastic kronecker and chung lu graph models"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "a model of internet topology using k shell decomposition"
    - "k core decomposition of internet graphs hierarchies self similarity and measurement biases"

Connected through hyperedge HE-4 (induced by source paper "structural sparsity of complex networks bounded expansion in random models and real world graphs"):
  with members:
    - "bidimensionality and kernels"
    - "a faster parameterized algorithm for treedepth"
    - "kernelization using structural parameters on sparse graph classes"
    - "deciding first order properties of nowhere dense graphs"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"
    - "linear kernels and single exponential algorithms via protrusion decompositions"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

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)