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: "seeded graph matching efficient algorithms and theoretical guarantees"
  Abstract: In this paper, a new information theoretic framework for graph matching is introduced. Using this framework, the graph isomorphism and seeded graph matching problems are studied. The maximum degree algorithm for graph isomorphism is analyzed and sufficient conditions for successful matching are rederived using type analysis. Furthermore, a new seeded matching algorithm with polynomial time complexity is introduced. The algorithm uses `typicality matching' and techniques from point-to-point communications for reliable matching. Assuming an Erdos-Renyi model on the correlated graph pair, it is shown that successful matching is guaranteed when the number of seeds grows logarithmically with the number of vertices in the graphs. The logarithmic coefficient is shown to be inversely proportional to the mutual information between the edge variables in the two graphs.

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"
    - "achieving perfect location privacy in wireless devices using anonymization"
    - "optimal de anonymization in random graphs with community structure"
    - "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 "a concentration of measure approach to database de anonymization"):
  with members:
    - "fundamental limits of database alignment"
    - "typicality matching for pairs of correlated graphs"
    - "matching anonymized and obfuscated time series to users profiles"
    - "an information theoretic framework for active de anonymization in social networks based on group memberships"
    - "where you are is who you are user identification by matching statistics"

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 "efficient and high quality seeded graph matching employing high order structural information"):
  with members:
    - "an iterative global structure assisted labeled network aligner"
    - "motifs in temporal networks"
    - "block models and personalized pagerank"
    - "higher order organization of complex networks"
    - "pagerank beyond the web"
    - "studying user footprints in different online social networks"
    - "defining and evaluating network communities based on ground truth"
    - "de anonymizing social networks"

Connected through hyperedge HE-5 (induced by source paper "typicality matching for pairs of correlated graphs"):
  with members:
    - "exact alignment recovery for correlated erdos renyi graphs"
    - "de anonymization of social networks with communities when quantifications meet algorithms"
    - "optimal de anonymization in random graphs with community structure"

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.IT(Information Theory)