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: "nearly efficient algorithms for the graph matching problem on correlated random graphs"
  Abstract: We give a quasipolynomial time algorithm for the graph matching problem (also known as noisy or robust graph isomorphism) on correlated random graphs. Specifically, for every $\gamma>0$, we give a $n^{O(\log n)}$ time algorithm that given a pair of $\gamma$-correlated $G(n,p)$ graphs $G_0,G_1$ with average degree between $n^{\varepsilon}$ and $n^{1/153}$ for $\varepsilon = o(1)$, recovers the "ground truth" permutation $\pi\in S_n$ that matches the vertices of $G_0$ to the vertices of $G_n$ in the way that minimizes the number of mismatched edges. We also give a recovery algorithm for a denser regime, and a polynomial-time algorithm for distinguishing between correlated and uncorrelated graphs. #R##N#Prior work showed that recovery is information-theoretically possible in this model as long the average degree was at least $\log n$, but sub-exponential time algorithms were only known in the dense case (i.e., for $p > n^{-o(1)}$). Moreover, "Percolation Graph Matching", which is the most common heuristic for this problem, has been shown to require knowledge of $n^{\Omega(1)}$ "seeds" (i.e., input/output pairs of the permutation $\pi$) to succeed in this regime. In contrast our algorithms require no seed and succeed for $p$ which is as low as $n^{o(1)-1}$.

Connected through hyperedge HE-1 (induced by source paper "seedless graph matching via tail of degree distribution for correlated erdos renyi graphs"):
  with members:
    - "seeded graph matching via large neighborhood statistics"
    - "efficient and high quality seeded graph matching employing high order structural information"
    - "analysis of a canonical labeling algorithm for the alignment of correlated erd h o s r enyi graphs"
    - "studying user footprints in different online social networks"
    - "de anonymizing social networks"

Connected through hyperedge HE-2 (induced by source paper "multiplex graph matching matched filters"):
  with members:
    - "partial recovery of erd h o s r e nyi graph alignment via k core alignment"
    - "exact alignment recovery for correlated erdos renyi graphs"
    - "optimal de anonymization in random graphs with community structure"
    - "on the reliability of profile matching across large online social networks"

Connected through hyperedge HE-3 (induced by source paper "analysis of a canonical labeling algorithm for the alignment of correlated erd h o s r enyi graphs"):
  with members:
    - "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"
    - "exact alignment recovery for correlated erdos renyi graphs"
    - "spectral alignment of graphs"
    - "optimal de anonymization in random graphs with community structure"
    - "an efficient reconciliation algorithm for social networks"

Connected through hyperedge HE-4 (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"
    - "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"
    - "exact alignment recovery for correlated erdos renyi graphs"
    - "spectral alignment of networks"

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)