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 "parallel batch dynamic graph connectivity"):
  Abstract: With the rapid growth of graph datasets over the past decade, a new kind of dynamic algorithm, supporting the ability to ingest batches of updates and exploit parallelism is needed in order to efficiently process large streams of updates. In this paper, we study batch and parallel algorithms for the dynamic connectivity problem, a fundamental problem that has received considerable attention in sequential setting. Perhaps the best known sequential algorithm is the elegant level-set algorithm of Holm, de Lichtenberg and Thorup (HDT), which achieves $O(\log^2 n)$ amortized time per edge insertion or deletion, and $O(\log n)$ time per query. #R##N#In this paper, we design a parallel batch-dynamic connectivity algorithm that is work-efficient with respect to the HDT algorithm for small batch sizes, and is asymptotically faster when the average batch size is sufficiently large. Given a sequence of batched updates, where $\Delta$ is the average batch size of all deletions, our algorithm achieves $O(\log n \log(1 + n / \Delta))$ expected amortized work per edge insertion and deletion and $O(\log^3 n)$ depth w.h.p. Our algorithm answers a batch of $k$ connectivity queries in $O(k \log(1 + n/k))$ expected work and $O(\log n)$ depth w.h.p. To the best of our knowledge, our algorithm is the first parallel batch-dynamic algorithm for connectivity.

Includes members:
  Member "theoretically efficient parallel graph algorithms can be fast and scalable":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relaxed scheduling for scalable belief propagation")
  Member "parallel working set search structures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal parallel algorithms in the binary forking model")
  Member "improved worst case deterministic parallel dynamic minimum spanning forest":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "work efficient batch incremental minimum spanning trees with applications to the sliding window model")
  Member "pam parallel augmented maps":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal parallel algorithms in the binary forking model")
  Member "batch parallel euler tour trees":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "batch dynamic algorithms via parallel change propagation and applications to dynamic trees")
  Member "near optimal parallel algorithms for dynamic dfs in undirected graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "depth first search in the semi streaming model")
  Member "the ubiquity of large graphs and surprising challenges of graph processing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries")
  Member "fully dynamic connectivity in o log n log log n 2 amortized expected time":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond")

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)