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: "graph connectivity in log diameter steps using label propagation"
  Abstract: The fastest deterministic algorithms for connected components take logarithmic time and perform superlinear work on a PRAM. These algorithms require pointer-chasing operations and are limited to shared-memory systems. Another popular method is `leader contraction' where non-leader vertices are contracted to adjacent leaders. The challenge is to select a constant fraction of leaders that are adjacent to a constant fraction of non-leaders with high probability. Instead we investigate whether simple label propagation can be as efficient as the fastest known algorithms for graph connectivity. Label propagation exchanges representative labels within a component. This is attractive for other models because it is deterministic and does not rely on pointer-chasing, but it is inherently difficult to complete in a sublinear number of steps. We are able to solve the problems with label propagation for graph connectivity. #R##N#We introduce a simple framework for deterministic graph connectivity in log-diameter steps using label propagation that is easily translated to other computational models. We present new algorithms in PRAM, Stream, and MapReduce. Given a simple, undirected graph $G=(V,E)$ with $n=|V|$ vertices, $m=|E|$ edges, and $D$ diameter, all our algorithms complete in $O(\log D)$ steps without pointer operations. We give the first label propagation algorithms that are competitive with the fastest PRAM algorithms, achieving $O(\log D)$ time and $O((m+n)\log D)$ work with $O(m+n)$ processors. Our main contribution is in Stream and MapReduce models. We give an efficient Stream-Sort algorithm that takes $O(\log D)$ passes and $O(\log n)$ memory, and a MapReduce algorithm taking $O(\log D)$ rounds and $O((m+n)\log D)$ communication overall. These are the first $O(\log D)$-step graph connectivity algorithms in Stream and MapReduce models that are also deterministic and simple to implement.

Connected through hyperedge HE-1 (induced by source paper "near optimal massively parallel graph connectivity"):
  with members:
    - "connected components on a pram in log diameter time"
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "approximating edit distance in truly subquadratic time quantum and mapreduce"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "connected components at scale via local contractions"
    - "massively parallel symmetry breaking on sparse graphs mis and maximal matching"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"

Connected through hyperedge HE-2 (induced by source paper "simple concurrent labeling algorithms for connected components"):
  with members:
    - "a randomized concurrent algorithm for disjoint set union"
    - "communication steps for parallel query processing"

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)