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 "hashing for statistics over k partitions"):
  Abstract: In this paper we analyze a hash function for $k$-partitioning a set into bins, obtaining strong concentration bounds for standard algorithms combining statistics from each bin. #R##N#This generic method was originally introduced by Flajolet and Martin~[FOCS'83] in order to save a factor $\Omega(k)$ of time per element over $k$ independent samples when estimating the number of distinct elements in a data stream. It was also used in the widely used HyperLogLog algorithm of Flajolet et al.~[AOFA'97] and in large-scale machine learning by Li et al.~[NIPS'12] for minwise estimation of set similarity. #R##N#The main issue of $k$-partition, is that the contents of different bins may be highly correlated when using popular hash functions. This means that methods of analyzing the marginal distribution for a single bin do not apply. Here we show that a tabulation based hash function, mixed tabulation, does yield strong concentration bounds on the most popular applications of $k$-partitioning similar to those we would get using a truly random hash function. The analysis is very involved and implies several new results of independent interest for both simple and double tabulation, e.g. a simple and efficient construction for invertible bloom filters and uniform hashing on a given set.

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 "beyond locality sensitive hashing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic streaming spectral sparsification in nearly linear time and space")
  Member "all distances sketches revisited hip estimators for massive graphs analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sampling sketches for concave sublinear functions of frequencies")
  Member "bottom k and priority sampling set similarity and subset sums with minimal independence":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "algorithms for similarity search and pseudorandomness")
  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 "biff bloom filter codes fast error correction for large data sets":
    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")
  Member "invertible bloom lookup tables":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)