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: "parallel working set search structures"
  Abstract: In this paper we present two versions of a parallel working-set map on p processors that supports searches, insertions and deletions. In both versions, the total work of all operations when the map has size at least p is bounded by the working-set bound, i.e., the cost of an item depends on how recently it was accessed (for some linearization): accessing an item in the map with recency r takes O(1+log r) work. In the simpler version each map operation has O+((log p)^2+log r) span (where n is the maximum size of the map). In the pipelined version each map operation on an item with recency r has O((log p)^2+log r\right)$ span. (Operations in parallel may have overlapping span; span is additive only for operations in sequence.) Both data structures are designed to be used by a dynamic multithreading parallel program that at each step executes a unit-time instruction or makes a data structure call. To achieve the stated bounds, the pipelined version requires a weak-priority scheduler, which supports a limited form of 2-level prioritization. At the end we explain how the results translate to practical implementations using work-stealing schedulers. To the best of our knowledge, this is the first parallel implementation of a self-adjusting search structure where the cost of an operation adapts to the access sequence. A corollary of the working-set bound is that it achieves work static optimality : the total work is bounded by the access costs in an optimal static search tree. A fuller version of this paper is at \urlhttp://arxiv.org/abs/1805.05787.

Connected through hyperedge HE-1 (induced by source paper "optimal parallel algorithms in the binary forking model"):
  with members:
    - "parallel finger search structures"
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "parallel write efficient algorithms and data structures for computational geometry"
    - "parallelism in randomized incremental algorithms"
    - "pam parallel augmented maps"
    - "resource oblivious sorting on multicores"
    - "parallel ordered sets using join"
    - "extending the nested parallel model to the nested dataflow model with provably efficient schedulers"

Connected through hyperedge HE-2 (induced by source paper "parallel batch dynamic graph connectivity"):
  with members:
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "improved worst case deterministic parallel dynamic minimum spanning forest"
    - "pam parallel augmented maps"
    - "batch parallel euler tour trees"
    - "near optimal parallel algorithms for dynamic dfs in undirected graphs"
    - "the ubiquity of large graphs and surprising challenges of graph processing"
    - "fully dynamic connectivity in o log n log log n 2 amortized expected time"
    - "fully dynamic minimum spanning forest with improved worst case update time"

Connected through hyperedge HE-3 (induced by source paper "parallel finger search structures"):
  with members:
    - "optimal parallel algorithms in the binary forking model"
    - "optimal multithreaded batch parallel 2 3 trees"
    - "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-4 (induced by source paper "optimal multithreaded batch parallel 2 3 trees"):
  with members:
    - "optimal parallel algorithms in the binary forking model"
    - "parallel finger 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)