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: "group detection in complex networks an algorithm and comparison of the state of the art"
  Abstract: Complex real-world networks commonly reveal characteristic groups of nodes like communities and modules. These are of value in various applications, especially in the case of large social and information networks. However, while numerous community detection techniques have been presented in the literature, approaches for other groups of nodes are relatively rare and often limited in some way. We present a simple propagation-based algorithm for general group detection that requires no a priori knowledge and has near ideal complexity. The main novelty here is that different types of groups are revealed through an adequate hierarchical group refinement procedure. The proposed algorithm is validated on various synthetic and real-world networks, and rigorously compared against twelve other state-of-the-art approaches on group detection, hierarchy discovery and link prediction tasks. The algorithm is comparable to the state of the art in community detection, while superior in general group detection and link prediction. Based on the comparison, we also discuss some prominent directions for future work on group detection in complex networks.

Connected through hyperedge HE-1 (induced by source paper "label propagation for clustering"):
  with members:
    - "corrected overlap weight and clustering coefficient"
    - "the many facets of community detection in complex networks"
    - "clustering scientific publications based on citation relations a systematic comparison of different methods"
    - "identifying robust communities and multi community nodes by combining top down and bottom up approaches to clustering"
    - "on the relationship between gaussian stochastic blockmodels and label propagation algorithms"
    - "labelrank a stabilized label propagation algorithm for community detection in networks"
    - "demon a local first discovery method for overlapping communities"
    - "towards linear time overlapping community detection in social networks"

Connected through hyperedge HE-2 (induced by source paper "clustering scientific publications based on citation relations a systematic comparison of different methods"):
  with members:
    - "citnetexplorer a new software tool for analyzing and visualizing citation networks"
    - "demon a local first discovery method for overlapping communities"
    - "defining and evaluating network communities based on ground truth"
    - "a new methodology for constructing a publication level classification system of science"
    - "efficient and principled method for detecting communities in networks"
    - "a unified approach to mapping and clustering of bibliometric networks"

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)