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: "analysis of a canonical labeling algorithm for the alignment of correlated erd h o s r enyi graphs"
  Abstract: Graph alignment in two correlated random graphs refers to the task of identifying the correspondence between vertex sets of the graphs. Recent results have characterized the exact information-theoretic threshold for graph alignment in correlated Erd\H{o}s-R\'enyi graphs. However, very little is known about the existence of efficient algorithms to achieve graph alignment without seeds. #R##N#In this work we identify a region in which a straightforward $O(n^{11/5} \log n )$-time canonical labeling algorithm, initially introduced in the context of graph isomorphism, succeeds in aligning correlated Erd\H{o}s-R\'enyi graphs. The algorithm has two steps. In the first step, all vertices are labeled by their degrees and a trivial minimum distance alignment (i.e., sorting vertices according to their degrees) matches a fixed number of highest degree vertices in the two graphs. Having identified this subset of vertices, the remaining vertices are matched using a alignment algorithm for bipartite graphs.

Connected through hyperedge HE-1 (induced by source paper "from tree matching to sparse graph alignment"):
  with members:
    - "spectral alignment of graphs"
    - "maximum quadratic assignment problem reduction from maximum label cover and lp based approximation algorithm"
    - "community detection thresholds and the weak ramanujan property"
    - "de anonymizing social networks"

Connected through hyperedge HE-2 (induced by source paper "seedless graph matching via tail of degree distribution for correlated erdos renyi graphs"):
  with members:
    - "nearly efficient algorithms for the graph matching problem on correlated random graphs"
    - "seeded graph matching via large neighborhood statistics"
    - "efficient and high quality seeded graph matching employing high order structural information"
    - "studying user footprints in different online social networks"
    - "de anonymizing social networks"

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"
    - "gemsec graph embedding with self clustering"
    - "exact alignment recovery for correlated erdos renyi graphs"
    - "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 "fundamental limits of database alignment"):
  with members:
    - "on the performance of a canonical labeling for matching correlated erdős renyi graphs"
    - "matching anonymized and obfuscated time series to users profiles"
    - "exact alignment recovery for correlated erdos renyi graphs"
    - "renyi divergence and kullback leibler divergence"

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.DS(Data Structures and Algorithms)