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: "optimal parallel algorithms in the binary forking model"
  Abstract: In this paper we develop optimal algorithms in the binary-forking model for a variety of fundamental problems, including sorting, semisorting, list ranking, tree contraction, range minima, and set union, intersection and difference. In the binary-forking model, tasks can only fork into two child tasks, but can do so recursively and asynchronously, and join up later. The tasks share memory, and costs are measured in terms of work (total number of instructions), and span (longest dependence chain). #R##N#Due to the asynchronous nature of the model, and a variety schedulers that are efficient in both theory and practice, variants of the model are widely used in practice in languages such as Cilk and Java Fork-Join. PRAM algorithms can be simulated in the model but at a loss of a factor of $\Omega(\log n)$ so most PRAM algorithms are not optimal in the model even if optimal on the PRAM. All algorithms we describe are optimal in work and span (logarithmic in span). Several are randomized. Beyond being the first optimal algorithms for their problems in the model, most are very simple.

Connected through hyperedge HE-1 (induced by source paper "parallel finger search structures"):
  with members:
    - "optimal multithreaded batch parallel 2 3 trees"
    - "parallel working set search structures"
    - "batch parallel euler tour trees"
    - "parallel combining benefits of explicit synchronization"
    - "a general technique for non blocking trees"
    - "fast parallel operations on search trees"
    - "flat combining and the synchronization parallelism tradeoff"

Connected through hyperedge HE-2 (induced by source paper "optimal multithreaded batch parallel 2 3 trees"):
  with members:
    - "parallel finger search structures"
    - "parallel working set search structures"
    - "fast parallel operations on search trees"

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)