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: "concurrent hash tables fast and general"
  Abstract: Concurrent hash tables are one of the most important concurrent data structures with numerous applications. Since hash table accesses can dominate the execution time of the overall application, we need implementations that achieve good speedup. Unfortunately, currently available concurrent hashing libraries turn out to be far away from this requirement in particular when contention on some elements occurs. #R##N#Our starting point for better performing data structures is a fast and simple lock-free concurrent hash table based on linear probing that is limited to word-sized key-value types and does not support dynamic size adaptation. We explain how to lift these limitations in a provably scalable way and demonstrate that dynamic growing has a performance overhead comparable to the same generalization in sequential hash tables. #R##N#We perform extensive experiments comparing the performance of our implementations with six of the most widely used concurrent hash tables. Ours are considerably faster than the best algorithms with similar restrictions and an order of magnitude faster than the best more general tables. In some extreme cases, the difference even approaches four orders of magnitude.

Connected through hyperedge HE-1 (induced by source paper "finding all global minimum cuts in practice"):
  with members:
    - "engineering kernelization for maximum cut"
    - "shared memory branch and reduce for multiterminal cuts"
    - "faster algorithms for edge connectivity via random 2 out contractions"
    - "wegotyoucovered the winning solver from the pace 2019 implementation challenge vertex cover track"
    - "exactly solving the maximum weight independent set problem on large real world graphs"
    - "experimental evaluation of parameterized algorithms for feedback vertex set"
    - "practical access to dynamic programming on tree decompositions"
    - "practical minimum cut algorithms"

Connected through hyperedge HE-2 (induced by source paper "concurrent expandable amqs on the basis of quotient filters"):
  with members:
    - "adaptive cuckoo filters"
    - "don t thrash how to cache your hash on flash"

Connected through hyperedge HE-3 (induced by source paper "hashgraph scalable hash tables using a sparse graph data structure"):
  with members:
    - "a dynamic hash table for the gpu"

Connected through hyperedge HE-4 (induced by source paper "practical minimum cut algorithms"):
  with members:
    - "local flow partitioning for faster edge connectivity"
    - "deterministic global minimum cut of a simple graph in near linear time"
    - "networkit an interactive tool suite for high performance network analysis"
    - "semi external algorithms for graph partitioning and clustering"
    - "on the analysis of a label propagation algorithm for community detection"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "minimum cuts in near linear time"

Connected through hyperedge HE-5 (induced by source paper "shared memory exact minimum cuts"):
  with members:
    - "practical minimum cut algorithms"
    - "local flow partitioning for faster edge connectivity"
    - "deterministic global minimum cut of a simple graph in near linear time"
    - "networkit an interactive tool suite for high performance network analysis"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-6 (induced by source paper "high quality shared memory graph partitioning"):
  with members:
    - "in place parallel super scalar samplesort ips 4 o"
    - "engineering parallel algorithms for community detection in massive networks"
    - "parallel graph partitioning for complex networks"
    - "partitioning complex networks via size constrained clustering"
    - "recent advances in graph partitioning"
    - "the power of simple tabulation hashing"
    - "n level graph partitioning"
    - "engineering multilevel graph partitioning algorithms"

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)