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: "optimal de anonymization in random graphs with community structure"
  Abstract: Anonymized social network graphs published for academic or advertisement purposes are subject to de-anonymization attacks by leveraging side information in the form of a second, public social network graph correlated with the anonymized graph. This is because the two are from the same underlying graph of true social relationships. In this paper, we (i) characterize the maximum a posteriori (MAP) estimates of user identities for the anonymized graph and (ii) provide sufficient conditions for successful de-anonymization for underlying graphs with community structure. Our results generalize prior work that assumed underlying graphs of Erd\H{o}s-R\'enyi type, in addition to proving the optimality of the attack strategy adopted in the prior work.

Connected through hyperedge HE-1 (induced by source paper "improving privacy in graphs through node addition"):
  with members:
    - "matching anonymized and obfuscated time series to users profiles"
    - "seeded graph matching efficient algorithms and theoretical guarantees"
    - "achieving perfect location privacy in wireless devices using anonymization"
    - "an efficient reconciliation algorithm for social networks"
    - "de anonymizing social networks"
    - "neighbor specific bgp more flexible routing policies while improving global stability"

Connected through hyperedge HE-2 (induced by source paper "multiplex graph matching matched filters"):
  with members:
    - "nearly efficient algorithms for the graph matching problem on correlated random graphs"
    - "partial recovery of erd h o s r e nyi graph alignment via k core alignment"
    - "exact alignment recovery for correlated erdos renyi graphs"
    - "on the reliability of profile matching across large online social networks"

Connected through hyperedge HE-3 (induced by source paper "privacy of dependent users against statistical matching"):
  with members:
    - "asymptotic limits of privacy in bayesian time series matching"
    - "a concentration of measure approach to database de anonymization"
    - "single server single message online private information retrieval with side information"
    - "asymptotic loss in privacy due to dependency in gaussian traces"
    - "fundamental limits of database alignment"
    - "community detection with side information exact recovery under the stochastic block model"
    - "matching graphs with community structure a concentration of measure approach"
    - "on the performance of a canonical labeling for matching correlated erdős renyi graphs"

Connected through hyperedge HE-4 (induced by source paper "analysis of a canonical labeling algorithm for the alignment of correlated erd h o s r enyi graphs"):
  with members:
    - "nearly efficient algorithms for the graph matching problem on correlated random graphs"
    - "seeded graph matching via large neighborhood statistics"
    - "reconciling graphs and sets of sets"
    - "an information theoretic framework for active de anonymization in social networks based on group memberships"
    - "exact alignment recovery for correlated erdos renyi graphs"
    - "spectral alignment of graphs"
    - "an efficient reconciliation algorithm for social networks"

Connected through hyperedge HE-5 (induced by source paper "typicality matching for pairs of correlated graphs"):
  with members:
    - "seeded graph matching efficient algorithms and theoretical guarantees"
    - "exact alignment recovery for correlated erdos renyi graphs"
    - "de anonymization of social networks with communities when quantifications meet algorithms"

Connected through hyperedge HE-6 (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"
    - "impact of clustering on the performance of network de anonymization"
    - "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-7 (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"
    - "impact of clustering on the performance of network de anonymization"
    - "de anonymizing social networks"

Connected through hyperedge HE-8 (induced by source paper "seeded graph matching efficient algorithms and theoretical guarantees"):
  with members:
    - "an information theoretic framework for active de anonymization in social networks based on group memberships"
    - "de anonymization of social networks with communities when quantifications meet algorithms"

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)