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 "batch parallel euler tour trees"):
  Abstract: The dynamic trees problem is to maintain a forest undergoing edge insertions and deletions while supporting queries for information such as connectivity. There are many existing data structures for this problem, but few of them are capable of exploiting parallelism in the batch-setting, in which large batches of edges are inserted or deleted from the forest at once. In this paper, we demonstrate that the Euler tour tree, an existing sequential dynamic trees data structure, can be parallelized in the batch setting. For a batch of $k$ updates over a forest of $n$ vertices, our parallel Euler tour trees perform $O(k \log (1 + n/k))$ expected work with $O(\log n)$ depth with high probability. Our work bound is asymptotically optimal, and we improve on the depth bound achieved by Acar et al. for the batch-parallel dynamic trees problem. #R##N#The main building block for parallelizing Euler tour trees is a batch-parallel skip list data structure, which we believe may be of independent interest. Euler tour trees require a sequence data structure capable of joins and splits. Sequentially, balanced binary trees are used, but they are difficult to join or split in parallel. We show that skip lists, on the other hand, support batches of joins or splits of size $k$ over $n$ elements with $O(k \log (1 + n/k))$ work in expectation and $O(\log n)$ depth with high probability. We also achieve the same efficiency bounds for augmented skip lists, which allows us to augment our Euler tour trees to support subtree queries. #R##N#Our data structures achieve between 67--96x self-relative speedup on 72 cores with hyper-threading on large batch sizes. Our data structures also outperform the fastest existing sequential dynamic trees data structures empirically.

Includes members:
  Member "improved worst case deterministic parallel dynamic minimum spanning forest":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "work efficient batch incremental minimum spanning trees with applications to the sliding window model")
  Member "work efficient parallel and incremental graph connectivity":
    also connected through other hyperedges:
      (none)
  Member "fast parallel operations on search trees":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "communication efficient weighted reservoir sampling from fully distributed data streams")
  Member "maintaining information in fully dynamic trees with top trees":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "coarse grained complexity for dynamic algorithms")

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)