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: "ged the method for group evolution discovery in social networks"
  Abstract: The continuous interest in the social network area contributes to the fast development of this field. The new possibilities of obtaining and storing data facilitate deeper analysis of the entire network, extracted social groups and single individuals as well. One of the most interesting research topic is the dynamics of social groups which means analysis of group evolution over time. Having appropriate knowledge and methods for dynamic analysis, one may attempt to predict the future of the group, and then manage it properly in order to achieve or change this predicted future according to specific needs. Such ability would be a powerful tool in the hands of human resource managers, personnel recruitment, marketing, etc. The social group evolution consists of individual events and seven types of such changes have been identified in the paper: continuing, shrinking, growing, splitting, merging, dissolving and forming. To enable the analysis of group evolution a change indicator—inclusion measure was proposed. It has been used in a new method for exploring the evolution of social groups, called Group Evolution Discovery (GED). The experimental results of its use together with the comparison to two well-known algorithms in terms of accuracy, execution time, flexibility and ease of implementation are also described in the paper.

Connected through hyperedge HE-1 (induced by source paper "a comparative study of different approaches for tracking communities in evolving social networks"):
  with members:
    - "different approaches to community evolution prediction in blogosphere"
    - "empirical comparison of algorithms for network community detection"

Connected through hyperedge HE-2 (induced by source paper "community discovery in dynamic networks a survey"):
  with members:
    - "stream graphs and link streams for the modeling of interactions over time"
    - "revealing evolutions in dynamical networks"
    - "the ground truth about metadata and community detection in networks"
    - "on the evaluation potential of quality functions in community detection for different contexts"
    - "a framework for the construction of generative models for mesoscale structure in multilayer networks"
    - "generative benchmark models for mesoscale structures in multilayer networks"
    - "enumerating maximal cliques in temporal graphs"
    - "using multiple criteria methods to evaluate community partitions"

Connected through hyperedge HE-3 (induced by source paper "evolutionary self expressive models for subspace clustering"):
  with members:
    - "evolutionary clustering via message passing"
    - "active orthogonal matching pursuit for sparse subspace clustering"
    - "theoretical analysis of sparse subspace clustering with missing entries"
    - "noisy subspace clustering via matching pursuits"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "high dimensional low rank plus sparse matrix decomposition"
    - "innovation pursuit a new approach to subspace clustering"
    - "a randomized greedy algorithm for near optimal sensor scheduling in large scale sensor networks"

Connected through hyperedge HE-4 (induced by source paper "analysis of group evolution prediction in complex networks"):
  with members:
    - "a comparative study of different approaches for tracking communities in evolving social networks"
    - "community discovery in dynamic networks a survey"
    - "wordnet2vec corpora agnostic word vectorization method"
    - "predicting community evolution in social networks"
    - "estimating continuous distributions in bayesian classifiers"
    - "different approaches to community evolution prediction in blogosphere"
    - "identification of group changes in blogosphere"
    - "predicting group evolution in the social network"

Connected through hyperedge HE-5 (induced by source paper "community structure characterization"):
  with members:
    - "from community detection to community profiling"
    - "on the evaluation potential of quality functions in community detection for different contexts"
    - "structure and inference in annotated networks"
    - "clustering attributed graphs models measures and methods"
    - "a community role approach to assess social capitalists visibility in the twitter network"
    - "interpreting communities based on the evolution of a dynamic attributed network"
    - "community detection in networks with node attributes"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-6 (induced by source paper "predicting community evolution in social networks"):
  with members:
    - "different approaches to community evolution prediction in blogosphere"
    - "identification of group changes in blogosphere"
    - "predicting group evolution in the social network"
    - "graphical analysis of social group dynamics"
    - "the slashdot zoo mining a social network with negative edges"

Connected through hyperedge HE-7 (induced by source paper "adaptive evolutionary clustering"):
  with members:
    - "a tutorial on spectral clustering"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-8 (induced by source paper "different approaches to community evolution prediction in blogosphere"):
  with members:
    - "estimating continuous distributions in bayesian classifiers"
    - "identification of group changes in blogosphere"
    - "predicting group evolution in the social network"
    - "influence of the dynamic social network timeframe type and size on the group evolution discovery"
    - "graphical analysis of social group dynamics"
    - "the slashdot zoo mining a social network with negative edges"

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)