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: "a degree centrality in multi layered social network"
  Abstract: Multi-layered social networks reflect complex relationships existing in modern interconnected IT systems. In such a network each pair of nodes may be linked by many edges that correspond to different communication or collaboration user activities. Multi-layered degree centrality for multi-layered social networks is presented in the paper. Experimental studies were carried out on data collected from the real Web 2.0 site. The multi-layered social network extracted from this data consists of ten distinct layers and the network analysis was performed for different degree centralities measures.

Connected through hyperedge HE-1 (induced by source paper "hierarchical stochastic block model for community detection in multiplex networks"):
  with members:
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "community extraction in multilayer networks with heterogeneous community structure"
    - "community detection in multiplex networks using locally adaptive random walks"
    - "producing a unified graph representation from multiple social network views"

Connected through hyperedge HE-2 (induced by source paper "principled multilayer network embedding"):
  with members:
    - "spectral clustering methods for multiplex networks"
    - "fast warped graph embedding unifying framework and one click algorithm"
    - "name disambiguation in anonymized graphs using network embedding"
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "multilayer spectral graph clustering via convex layer aggregation"
    - "higher order organization of complex networks"
    - "node2vec scalable feature learning for networks"
    - "clustering network layers with the strata multilayer stochastic block model"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "the international postal network and other global flows as proxies for national wellbeing"):
  with members:
    - "keep your friends close and your facebook friends closer a multiplex network approach to the analysis of offline and online social ties"
    - "big questions for social media big data representativeness validity and other methodological pitfalls"
    - "the anatomy of the facebook social graph"
    - "dimes let the internet measure itself"

Connected through hyperedge HE-5 (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 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-6 (induced by source paper "analysis of neighbourhoods in multi layered dynamic social networks"):
  with members:
    - "shortest path discovery in the 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)