human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "nearly efficient algorithms for the graph matching problem on correlated random graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "seedless graph matching via tail of degree distribution for correlated erdos renyi graphs")
  Member "seeded graph matching via large neighborhood statistics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "seedless graph matching via tail of degree distribution for correlated erdos renyi graphs")
  Member "reconciling graphs and sets of sets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust set reconciliation via locality sensitive hashing")
  Member "an information theoretic framework for active de anonymization in social networks based on group memberships":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a concentration of measure approach to database de anonymization")
  Member "exact alignment recovery for correlated erdos renyi graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multiplex graph matching matched filters")
  Member "spectral alignment of graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "elruna elimination rule based network alignment")
  Member "optimal de anonymization in random graphs with community structure":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improving privacy in graphs through node addition")
  Member "an efficient reconciliation algorithm for social networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the privacy of dk random graphs")

Question: Please tell me which class the source paper 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)