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: "exact alignment recovery for correlated erdos renyi graphs"
  Abstract: We consider the problem of perfectly recovering the vertex correspondence between two correlated \ER (ER) graphs on the same vertex set. The correspondence between the vertices can be obscured by randomly permuting the vertex labels of one of the graphs. We determine the information-theoretic threshold for exact recovery, i.e. the conditions under which the entire vertex correspondence can be correctly recovered given unbounded computational resources.

Connected through hyperedge HE-1 (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"
    - "optimal de anonymization in random graphs with community structure"
    - "on the reliability of profile matching across large online social networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "spectre seedless network alignment via spectral centralities"):
  with members:
    - "regal representation learning based graph alignment"
    - "seeded graph matching via large neighborhood statistics"
    - "an iterative global structure assisted labeled network aligner"
    - "analysis of a canonical labeling algorithm for the alignment of correlated erd h o s r enyi graphs"
    - "gemsec graph embedding with self clustering"
    - "graph isomorphism in quasipolynomial time"
    - "spectral alignment of networks"
    - "maximum quadratic assignment problem reduction from maximum label cover and lp based approximation algorithm"

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"
    - "spectral alignment of graphs"
    - "optimal de anonymization in random graphs with community structure"
    - "an efficient reconciliation algorithm for social networks"

Connected through hyperedge HE-5 (induced by source paper "partial recovery of erd h o s r e nyi graph alignment via k core alignment"):
  with members:
    - "fundamental limits of database alignment"
    - "nearly efficient algorithms for the graph matching problem on correlated random graphs"
    - "seeded graph matching via large neighborhood statistics"
    - "typicality matching for pairs of correlated graphs"
    - "blind de anonymization attacks using social networks"
    - "the computer science and physics of community detection landscapes phase transitions and hardness"
    - "spectral alignment of networks"

Connected through hyperedge HE-6 (induced by source paper "nearly efficient algorithms for the graph matching problem on correlated random graphs"):
  with members:
    - "seeded graph matching via large neighborhood statistics"
    - "the power of sum of squares for detecting hidden structures"
    - "hardness of robust graph isomorphism lasserre gaps and asymmetry of random graphs"
    - "an efficient reconciliation algorithm for social networks"
    - "de anonymizing social networks"

Connected through hyperedge HE-7 (induced by source paper "fundamental limits of database alignment"):
  with members:
    - "on the performance of a canonical labeling for matching correlated erdős renyi graphs"
    - "analysis of a canonical labeling algorithm for the alignment of correlated erd h o s r enyi graphs"
    - "matching anonymized and obfuscated time series to users profiles"
    - "renyi divergence and kullback leibler divergence"

Connected through hyperedge HE-8 (induced by source paper "typicality matching for pairs of correlated graphs"):
  with members:
    - "seeded graph matching efficient algorithms and theoretical guarantees"
    - "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)