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: "low distortion inference of latent similarities from a multiplex social network"
  Abstract: Much of social network analysis is - implicitly or explicitly - predicated on the assumption that individuals tend to be more similar to their friends than to strangers. Thus, an observed social network provides a noisy signal about the latent underlying "social space:" the way in which individuals are similar or dissimilar. Many research questions frequently addressed via social network analysis are in reality questions about this social space, raising the question of inverting the process: Given a social network, how accurately can we reconstruct the social structure of similarities and dissimilarities? #R##N#We begin to address this problem formally. Observed social networks are usually multiplex, in the sense that they reflect (dis)similarities in several different "categories," such as geographical proximity, kinship, or similarity of professions/hobbies. We assume that each such category is characterized by a latent metric capturing (dis)similarities in this category. Each category gives rise to a separate social network: a random graph parameterized by this metric. For a concrete model, we consider Kleinberg's small world model and some variations thereof. The observed social network is the unlabeled union of these graphs, i.e., the presence or absence of edges can be observed, but not their origins. Our main result is an algorithm which reconstructs each metric with provably low distortion.

Connected through hyperedge HE-1 (induced by source paper "a method for group extraction and analysis in multilayer social networks"):
  with members:
    - "group evolution discovery in social networks"
    - "estimating continuous distributions in bayesian classifiers"
    - "tracking group evolution in social networks"
    - "analysis of neighbourhoods in multi layered dynamic social networks"
    - "a degree centrality in multi layered social network"
    - "shortest path discovery in the multi layered social network"
    - "exposing multi relational networks to single relational network analysis algorithms"
    - "social networks that matter twitter under the microscope"

Connected through hyperedge HE-2 (induced by source paper "extraction of multilayered social networks from activity data"):
  with members:
    - "exploiting all phone media a multidimensional network analysis of phone users sociality"
    - "multi layered social networks"
    - "analysis of neighbourhoods in multi layered dynamic social networks"
    - "a degree centrality in multi layered social network"
    - "a classification for community discovery methods in complex networks"
    - "shortest path discovery in the multi layered social network"
    - "multidimensional social network in the social recommender system"
    - "social networks that matter twitter under the microscope"

Connected through hyperedge HE-3 (induced by source paper "a tensor approach to learning mixed membership community models"):
  with members:
    - "dirichlet draws are sparse with high probability"
    - "fast detection of overlapping communities via online tensor methods"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "breaking the small cluster barrier of graph clustering"
    - "a method of moments for mixture models and hidden markov models"
    - "a spectral algorithm for latent dirichlet allocation"
    - "statistical algorithms and a lower bound for detecting planted clique"
    - "finding overlapping communities in social networks toward a rigorous approach"

Connected through hyperedge HE-4 (induced by source paper "finding endogenously formed communities"):
  with members:
    - "finding overlapping communities in social networks toward a rigorous approach"
    - "empirical comparison of algorithms for network community detection"
    - "reducibility among fractional stability problems"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-5 (induced by source paper "finding overlapping communities in social networks toward a rigorous approach"):
  with members:
    - "overlapping community detection in networks the state of the art and comparative study"
    - "triangles to capture social cohesion"
    - "the hitchhiker s guide to affiliation networks a game theoretic approach"
    - "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)