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: "high speed hashing for integers and strings"
  Abstract: These notes describe the most efficient hash functions currently known for hashing integers and strings. These modern hash functions are often an order of magnitude faster than those presented in standard text books. They are also simpler to implement, and hence a clear win in practice, but their analysis is harder. Some of the most practical hash functions have only appeared in theory papers, and some of them requires combining results from different theory papers. The goal here is to combine the information in lecture-style notes that can be used by theoreticians and practitioners alike, thus making these practical fruits of theory more widely accessible.

Connected through hyperedge HE-1 (induced by source paper "peeling close to the orientability threshold spatial coupling in hashing based data structures"):
  with members:
    - "dense peelable random uniform hypergraphs"
    - "fast scalable construction of minimal perfect hash functions"
    - "bubing massive crawling for the masses"
    - "the multiple orientability thresholds for random hypergraphs"
    - "on the insertion time of random walk cuckoo hashing"
    - "cache oblivious peeling of random hypergraphs"
    - "thresholds of spatially coupled systems via lyapunov s method"
    - "a simple proof of threshold saturation for coupled scalar recursions"

Connected through hyperedge HE-2 (induced by source paper "fast hashing with strong concentration bounds"):
  with members:
    - "optimal streaming and tracking distinct elements with high probability"
    - "faster 64 bit universal hashing using carry less multiplications"
    - "pseudorandomness via the discrete fourier transform"
    - "from independence to expansion and back again"
    - "generating k independent variables in constant time"
    - "hashing for statistics over k partitions"
    - "bottom k and priority sampling set similarity and subset sums with minimal independence"
    - "simple tabulation fast expanders double tabulation and high independence"

Connected through hyperedge HE-3 (induced by source paper "non empty bins with simple tabulation hashing"):
  with members:
    - "fast and powerful hashing using tabulation"
    - "hashing for statistics over k partitions"
    - "simple tabulation fast expanders double tabulation and high independence"

Connected through hyperedge HE-4 (induced by source paper "massively parallel similarity join edge isoperimetry and distance correlations on the hypercube"):
  with members:
    - "scalability and total recall with fast coveringlsh"
    - "learning to hash for indexing big data a survey"
    - "locality sensitive hashing without false negatives"
    - "probabilistic polynomials and hamming nearest neighbors"
    - "lower bounds on near neighbor search via metric expansion"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"

Connected through hyperedge HE-5 (induced by source paper "regular and almost universal hashing an efficient implementation"):
  with members:
    - "faster 64 bit universal hashing using carry less multiplications"
    - "strongly universal string hashing is fast"
    - "the universality of iterated hashing over variable length strings"
    - "recursive n gram hashing is pairwise independent at best"

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)