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 "seedless graph matching via tail of degree distribution for correlated erdos renyi graphs"):
  Abstract: The graph matching problem refers to recovering the node-to-node correspondence between two correlated graphs. A previous work theoretically showed that recovering is feasible in sparse Erdos-Renyi graphs if and only if the probability of having an edge between a pair of nodes in one of the graphs and also between the corresponding nodes in the other graph is in the order of $\Omega(\log(n)/n)$, where n is the number of nodes. In this paper, we propose a graph matching algorithm which obtains correct matching with high probability in Erdos-Renyi graphs for the region of $\Theta(\log(n)/n)$ without using a seed set of pre-matched node pairs as an input. The algorithm assigns structurally innovative features to high-degree nodes based on the tail of empirical degree distribution of their neighbor nodes. Then, it matches the high-degree nodes according to these features, and finally obtains a matching for the remaining nodes. We evaluate the performance of proposed algorithm in the regions of $\Theta(\log(n)/n)$ and $\Theta(\log^{2}(n)/n)$. Experiments show that it outperforms previous works in both regions.

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 "multiplex graph matching matched filters")
  Member "seeded graph matching via large neighborhood statistics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spectre seedless network alignment via spectral centralities")
  Member "efficient and high quality seeded graph matching employing high order structural information":
    also connected through other hyperedges:
      (none)
  Member "analysis of a canonical labeling algorithm for the alignment of correlated erd h o s r enyi graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "from tree matching to sparse graph alignment")
  Member "studying user footprints in different online social networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "graph neural networks for user identity linkage")
  Member "de anonymizing social networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "knowledge 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)