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: "linear probing with constant independence"
  Abstract: Hashing with linear probing dates back to the 1950s, and is among the most studied algorithms. In recent years it has become one of the most important hash table organizations since it uses the cache of modern computers very well. Unfortunately, previous analysis rely either on complicated and space consuming hash functions, or on the unrealistic assumption of free access to a truly random hash function. Already Carter and Wegman, in their seminal paper on universal hashing, raised the question of extending their analysis to linear probing. However, we show in this paper that linear probing using a pairwise independent family may have expected {\em logarithmic} cost per operation. On the positive side, we show that 5-wise independence is enough to ensure constant expected time per operation. This resolves the question of finding a space and time efficient hash function that provably ensures good performance for linear probing.

Connected through hyperedge HE-1 (induced by source paper "tight static lower bounds for non adaptive data structures"):
  with members:
    - "static data structure lower bounds imply rigidity"
    - "crossing the logarithmic barrier for dynamic boolean data structure lower bounds"
    - "amortized dynamic cell probe lower bounds from four party communication"
    - "set membership with non adaptive bit probes"
    - "set membership with a few bit probes"
    - "the cell probe complexity of dynamic range counting"
    - "lower bounds on near neighbor search via metric expansion"
    - "the power of simple tabulation hashing"

Connected through hyperedge HE-2 (induced by source paper "secretary ranking with minimal inversions"):
  with members:
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "top k ranking from pairwise comparisons when spectral ranking is optimal"
    - "parallel algorithms for select and partition with noisy comparisons"
    - "beyond matroids secretary problem and prophet inequality with general constraints"
    - "secretary problems with non uniform arrival order"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "pricing online decisions beyond auctions"
    - "simple robust and optimal ranking from pairwise comparisons"

Connected through hyperedge HE-3 (induced by source paper "fast and powerful hashing using tabulation"):
  with members:
    - "from independence to expansion and back again"
    - "the power of two choices with simple tabulation"
    - "generating k independent variables in constant time"
    - "approximately minwise independence with twisted tabulation"
    - "hashing for statistics over k partitions"
    - "simple tabulation fast expanders double tabulation and high independence"
    - "on the k independence required by linear probing and minwise independence"
    - "invertible bloom lookup tables"

Connected through hyperedge HE-4 (induced by source paper "quicksort largest bucket and min wise hashing with limited independence"):
  with members:
    - "from independence to expansion and back again"
    - "generating k independent variables in constant time"
    - "simple tabulation fast expanders double tabulation and high independence"
    - "on the k independence required by linear probing and minwise independence"
    - "the power of simple tabulation hashing"

Connected through hyperedge HE-5 (induced by source paper "bottom k and priority sampling set similarity and subset sums with minimal independence"):
  with members:
    - "on the k independence required by linear probing and minwise independence"
    - "even better framework for min wise based algorithms"
    - "fast pseudo random fingerprints"
    - "stream sampling for variance optimal estimation of subset sums"

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)