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: "shortest path discovery in the multi layered social network"
  Abstract: Multi-layered social networks consist of the fixed set of nodes linked by multiple connections. These connections may be derived from different types of user activities logged in the IT system. To calculate any structural measures for multi-layered networks this multitude of relations should be coped with in the parameterized way. Two separate algorithms for evaluation of shortest paths in the multi-layered social network are proposed in the paper. The first one is based on pre-processing -- aggregation of multiple links into single multi-layered edges, whereas in the second approach, many edges are processed 'on the fly' in the middle of path discovery. Experimental studies carried out on the DBLP database are presented as well.

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"
    - "low distortion inference of latent similarities from a multiplex social network"
    - "a degree centrality in 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 "clustering attributed graphs models measures and methods"):
  with members:
    - "overlapping community detection in networks the state of the art and comparative study"
    - "abacus frequent pattern mining based community discovery in multidimensional networks"
    - "combinatorial analysis of multiple networks"
    - "an axiomatic study of objective functions for graph clustering"
    - "community detection in networks with node attributes"
    - "efficient community detection in large networks using content and links"
    - "a classification for community discovery methods in complex networks"
    - "predicting links and inferring attributes using a social attribute network san"

Connected through hyperedge HE-3 (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"
    - "low distortion inference of latent similarities from a multiplex social network"
    - "a degree centrality in multi layered social network"
    - "a classification for community discovery methods in complex networks"
    - "multidimensional social network in the social recommender system"
    - "social networks that matter twitter under the microscope"

Connected through hyperedge HE-4 (induced by source paper "combinatorial analysis of multiple networks"):
  with members:
    - "one none and one hundred thousand profiles re imagining the pirandellian identity dilemma in the era of online social networks"

Connected through hyperedge HE-5 (induced by source paper "analysis of neighbourhoods in multi layered dynamic social networks"):
  with members:
    - "a degree centrality in multi layered social network"
    - "multidimensional social network in the social recommender system"
    - "exposing multi relational networks to single relational network analysis algorithms"
    - "social networks that matter twitter under the microscope"
    - "a multi relational network to support the scholarly communication process"

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)