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: "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.

Connected through hyperedge HE-1 (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"
    - "high speed hashing for integers and strings"
    - "from independence to expansion and back again"
    - "generating k independent variables in constant time"
    - "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-2 (induced by source paper "fully understanding the hashing trick"):
  with members:
    - "optimality of the johnson lindenstrauss lemma"
    - "simple tabulation fast expanders double tabulation and high independence"
    - "random projections for linear support vector machines"
    - "sparsity lower bounds for dimensionality reducing maps"
    - "a sparse johnson lindenstrauss transform"
    - "sparser johnson lindenstrauss transforms"
    - "feature hashing for large scale multitask learning"

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

Connected through hyperedge HE-4 (induced by source paper "fast similarity sketching"):
  with members:
    - "optimal densification for fast and accurate minwise hashing"
    - "set similarity search beyond minhash"
    - "optimal data dependent hashing for approximate near neighbors"
    - "bottom k and priority sampling set similarity and subset sums with minimal independence"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-5 (induced by source paper "optimal densification for fast and accurate minwise hashing"):
  with members:
    - "ssh sketch shingle hash for indexing massive scale time series"
    - "the optimality of correlated sampling"
    - "consistent weighted sampling made fast small and easy"

Connected through hyperedge HE-6 (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"
    - "simple tabulation fast expanders double tabulation and high independence"
    - "on the k independence required by linear probing and minwise independence"
    - "invertible bloom lookup tables"
    - "ams without 4 wise independence on product domains"

Connected through hyperedge HE-7 (induced by source paper "the power of two choices with simple tabulation"):
  with members:
    - "from independence to expansion and back again"
    - "simple tabulation fast expanders double tabulation and high independence"

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)