human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "fast and powerful hashing using tabulation"):
  Abstract: Randomized algorithms are often enjoyed for their simplicity, but the hash functions employed to yield the desired probabilistic guarantees are often too complicated to be practical. Here we survey recent results on how simple hashing schemes based on tabulation provide unexpectedly strong guarantees. #R##N#Simple tabulation hashing dates back to Zobrist [1970]. Keys are viewed as consisting of $c$ characters and we have precomputed character tables $h_1,...,h_c$ mapping characters to random hash values. A key $x=(x_1,...,x_c)$ is hashed to $h_1[x_1] \oplus h_2[x_2].....\oplus h_c[x_c]$. This schemes is very fast with character tables in cache. While simple tabulation is not even 4-independent, it does provide many of the guarantees that are normally obtained via higher independence, e.g., linear probing and Cuckoo hashing. #R##N#Next we consider twisted tabulation where one input character is "twisted" in a simple way. The resulting hash function has powerful distributional properties: Chernoff-Hoeffding type tail bounds and a very small bias for min-wise hashing. This also yields an extremely fast pseudo-random number generator that is provably good for many classic randomized algorithms and data-structures. #R##N#Finally, we consider double tabulation where we compose two simple tabulation functions, applying one to the output of the other, and show that this yields very high independence in the classic framework of Carter and Wegman [1977]. In fact, w.h.p., for a given set of size proportional to that of the space consumed, double tabulation gives fully-random hashing. We also mention some more elaborate tabulation schemes getting near-optimal independence for given time and space. #R##N#While these tabulation schemes are all easy to implement and use, their analysis is not.

Includes members:
  Member "from independence to expansion and back again":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "algorithms for similarity search and pseudorandomness")
  Member "the power of two choices with simple tabulation":
    also connected through other hyperedges:
      (none)
  Member "generating k independent variables in constant time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "algorithms for similarity search and pseudorandomness")
  Member "approximately minwise independence with twisted tabulation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "range efficient consistent sampling and locality sensitive hashing for polygons")
  Member "hashing for statistics over k partitions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast hashing with strong concentration bounds")
  Member "simple tabulation fast expanders double tabulation and high independence":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "algorithms for similarity search and pseudorandomness")
  Member "on the k independence required by linear probing and minwise independence":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secretary ranking with minimal inversions")
  Member "invertible bloom lookup tables":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "peeling close to the orientability threshold spatial coupling in hashing based data structures")

Question: Please tell me which class the source paper 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)