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: "multigraph sampling of online social networks"
  Abstract: State-of-the-art techniques for probability sampling of users of online social networks (OSNs) are based on random walks on a single social relation (typically friendship). While powerful, these methods rely on the social graph being fully connected. Furthermore, the mixing time of the sampling process strongly depends on the characteristics of this graph. In this paper, we observe that there often exist other relations between OSN users, such as membership in the same group or participation in the same event. We propose to exploit the graphs these relations induce, by performing a random walk on their union multigraph. We design a computationally efficient way to perform multigraph sampling by randomly selecting the graph on which to walk at each iteration. We demonstrate the benefits of our approach through (i) simulation in synthetic graphs, and (ii) measurements of Last.fm - an Internet website for music with social networking features. More specifically, we show that multigraph sampling can obtain a representative sample and faster convergence, even when the individual graphs fail, i.e., are disconnected or highly clustered.

Connected through hyperedge HE-1 (induced by source paper "sampling online social networks by random walk with indirect jumps"):
  with members:
    - "inferring networks of substitutable and complementary products"
    - "leveraging history for faster sampling of online social networks"
    - "romantic partnerships and the dispersion of social ties a network analysis of relationship status on facebook"
    - "efficiently estimating motif statistics of large networks"
    - "triadic measures on graphs the power of wedge sampling"
    - "faster random walks by rewiring online social networks on the fly"
    - "estimating and sampling graphs with multidimensional random walks"

Connected through hyperedge HE-2 (induced by source paper "do we really need to catch them all a new user guided social media crawling method"):
  with members:
    - "finding influential users in social media using association rule learning"
    - "sampling online social networks via heterogeneous statistics"
    - "predicting community evolution in social networks"
    - "crawling facebook for social network analysis purposes"

Connected through hyperedge HE-3 (induced by source paper "spreading processes in multilayer networks"):
  with members:
    - "clustering attributed graphs models measures and methods"
    - "multidimensional epidemic thresholds in diffusion processes over interdependent networks"
    - "multiple routes transmitted epidemics on multiplex networks"
    - "measuring large scale social networks with high resolution"
    - "social bootstrapping how pinterest and last fm social communities benefit by borrowing links from facebook"
    - "diffusion of innovations over multiplex social networks"
    - "cooperation optimized design for information dissemination in vehicular networks using evolutionary game theory"
    - "exploiting cellular data for disease containment and information campaigns strategies in country wide epidemics"

Connected through hyperedge HE-4 (induced by source paper "evolution of directed triangle motifs in the google osn"):
  with members:
    - "degree relations of triangles in real world networks and graph models"
    - "evolution of social attribute networks measurements modeling and implications using google"
    - "google or google dissecting the evolution of the new osn in its first year"
    - "systematic topology analysis and generation using degree correlations"

Connected through hyperedge HE-5 (induced by source paper "sampling online social networks via heterogeneous statistics"):
  with members:
    - "walking on a graph with a magnifying glass stratified sampling via weighted random walks"
    - "estimating and sampling graphs with multidimensional random walks"

Connected through hyperedge HE-6 (induced by source paper "design of efficient sampling methods on hybrid social affiliation networks"):
  with members:
    - "triadic measures on graphs the power of wedge sampling"
    - "faster random walks by rewiring online social networks on the fly"
    - "twitter mood predicts the stock market"
    - "estimating and sampling graphs with multidimensional random walks"

Connected through hyperedge HE-7 (induced by source paper "a survey and taxonomy of graph sampling"):
  with members:
    - "identifying communities and key vertices by reconstructing networks from samples"
    - "graph size estimation"
    - "coarse grained topology estimation via graph sampling"
    - "triangles to capture social cohesion"
    - "towards unbiased bfs sampling"
    - "estimating and sampling graphs with multidimensional random walks"
    - "twice ramanujan sparsifiers"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "a measurement framework for directed networks"):
  with members:
    - "diffusion aware sampling and estimation in information diffusion networks"
    - "sampling from diffusion networks"
    - "towards unbiased bfs sampling"
    - "estimating and sampling graphs with multidimensional random walks"

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.NI(Networking and Internet Architecture)