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: "efficient construction of probabilistic tree embeddings"
  Abstract: In this paper we describe an algorithm that embeds a graph metric $(V,d_G)$ on an undirected weighted graph $G=(V,E)$ into a distribution of tree metrics $(T,D_T)$ such that for every pair $u,v\in V$, $d_G(u,v)\leq d_T(u,v)$ and ${\bf{E}}_{T}[d_T(u,v)]\leq O(\log n)\cdot d_G(u,v)$. Such embeddings have proved highly useful in designing fast approximation algorithms, as many hard problems on graphs are easy to solve on tree instances. For a graph with $n$ vertices and $m$ edges, our algorithm runs in $O(m\log n)$ time with high probability, which improves the previous upper bound of $O(m\log^3 n)$ shown by Mendel et al.\,in 2009. #R##N#The key component of our algorithm is a new approximate single-source shortest-path algorithm, which implements the priority queue with a new data structure, the "bucket-tree structure". The algorithm has three properties: it only requires linear time in the number of edges in the input graph; the computed distances have a distance preserving property; and when computing the shortest-paths to the $k$-nearest vertices from the source, it only requires to visit these vertices and their edge lists. These properties are essential to guarantee the correctness and the stated time bound. #R##N#Using this shortest-path algorithm, we show how to generate an intermediate structure, the approximate dominance sequences of the input graph, in $O(m \log n)$ time, and further propose a simple yet efficient algorithm to converted this sequence to a tree embedding in $O(n\log n)$ time, both with high probability. Combining the three subroutines gives the stated time bound of the algorithm. #R##N#Then we show that this efficient construction can facilitate some applications. We proved that FRT trees (the generated tree embedding) are Ramsey partitions with asymptotically tight bound, so the construction of a series of distance oracles can be accelerated.

Connected through hyperedge HE-1 (induced by source paper "the power of recourse better algorithms for facility location in online and dynamic models"):
  with members:
    - "a tree structure for dynamic facility location"
    - "facility location problem in differential privacy model revisited"
    - "online facility location with deletions"
    - "online bipartite matching with amortized o log 2 n replacements"
    - "online and dynamic algorithms for set cover"
    - "facility location in evolving metrics"
    - "online network design algorithms via hierarchical decompositions"
    - "dynamic facility location via exponential clocks"

Connected through hyperedge HE-2 (induced by source paper "improved parallel cache oblivious algorithms for dynamic programming and linear algebra"):
  with members:
    - "persistent memory i o primitives"
    - "parallel write efficient algorithms and data structures for computational geometry"
    - "algorithmic building blocks for asymmetric memories"
    - "the parallel persistent memory model"
    - "resource oblivious sorting on multicores"
    - "implicit decomposition for write efficient connectivity algorithms"
    - "on the fine grained complexity of one dimensional dynamic programming"
    - "extending the nested parallel model to the nested dataflow model with provably efficient schedulers"

Connected through hyperedge HE-3 (induced by source paper "parallelism in randomized incremental algorithms"):
  with members:
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "parallel shortest paths using radius stepping"
    - "parallel correlation clustering on big graphs"
    - "scalable influence estimation in continuous time diffusion networks"
    - "greedy sequential maximal independent set and matching are parallel on average"

Connected through hyperedge HE-4 (induced by source paper "parallel metric tree embedding based on an algebraic view on moore bellman ford"):
  with members:
    - "parallelism in randomized incremental algorithms"
    - "near optimal approximate shortest paths and transshipment in distributed and streaming models"
    - "a hierarchy of lower bounds for sublinear additive spanners"
    - "hopsets with constant hopbound and applications to approximate shortest paths"
    - "an almost tight distributed algorithm for computing single source shortest paths"
    - "a deterministic almost tight distributed algorithm for approximating single source shortest paths"
    - "fast partial distance estimation and applications"
    - "powers of tensors and fast matrix multiplication"

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)