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: "impact of clustering on the performance of network de anonymization"
  Abstract: Recently, graph matching algorithms have been successfully applied to the problem of network de-anonymization, in which nodes (users) participating to more than one social network are identified only by means of the structure of their links to other members. This procedure exploits an initial set of seed nodes large enough to trigger a percolation process which correctly matches almost all other nodes across the different social networks. Our main contribution is to show the crucial role played by clustering, which is a ubiquitous feature of realistic social network graphs (and many other systems). Clustering has both the effect of making matching algorithms more vulnerable to errors, and the potential to dramatically reduce the number of seeds needed to trigger percolation, thanks to a wave-like propagation effect. We demonstrate these facts by considering a fairly general class of random geometric graphs with variable clustering level, and showing how clever algorithms can achieve surprisingly good performance while containing matching errors.

Connected through hyperedge HE-1 (induced by source paper "blind de anonymization attacks using social networks"):
  with members:
    - "overlapping community detection in networks the state of the art and comparative study"
    - "de anonymizing social networks"
    - "systematic topology analysis and generation using degree correlations"

Connected through hyperedge HE-2 (induced by source paper "de anonymizing social networks with overlapping community structure"):
  with members:
    - "de anonymization of social networks with communities when quantifications meet algorithms"
    - "optimal de anonymization in random graphs with community structure"
    - "on the relation between identifiability differential privacy and mutual information privacy"
    - "exact recovery in the stochastic block model"
    - "defining and evaluating network communities based on ground truth"
    - "de anonymizing social networks"
    - "a path following algorithm for the graph matching problem"

Connected through hyperedge HE-3 (induced by source paper "de anonymization of social networks with communities when quantifications meet algorithms"):
  with members:
    - "the value of privacy strategic data subjects incentive mechanisms and fundamental limits"
    - "optimal de anonymization in random graphs with community structure"
    - "de anonymizing social 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)