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 hashing with strong concentration bounds"):
  Abstract: Previous work on tabulation hashing by P{\v a}tra{\c s}cu and Thorup from STOC'11 on simple tabulation and from SODA'13 on twisted tabulation offered Chernoff-style concentration bounds on hash based sums, e.g., the number of balls/keys hashing to a given bin, but under some quite severe restrictions on the expected values of these sums. The basic idea in tabulation hashing is to view a key as consisting of $c=O(1)$ characters, e.g., a 64-bit key as $c=8$ characters of 8-bits. The character domain $\Sigma$ should be small enough that character tables of size $|\Sigma|$ fit in fast cache. The schemes then use $O(1)$ tables of this size, so the space of tabulation hashing is $O(|\Sigma|)$. However, the concentration bounds by P{\v a}tra{\c s}cu and Thorup only apply if the expected sums are $\ll |\Sigma|$. #R##N#To see the problem, consider the very simple case where we use tabulation hashing to throw $n$ balls into $m$ bins and want to analyse the number of balls in a given bin. With their concentration bounds, we are fine if $n=m$, for then the expected value is $1$. However, if $m=2$, as when tossing $n$ unbiased coins, the expected value $n/2$ is $\gg |\Sigma|$ for large data sets, e.g., data sets that do not fit in fast cache. #R##N#To handle expectations that go beyond the limits of our small space, we need a much more advanced analysis of simple tabulation, plus a new tabulation technique that we call \emph{tabulation-permutation} hashing which is at most twice as slow as simple tabulation. No other hashing scheme of comparable speed offers similar Chernoff-style concentration bounds.

Includes members:
  Member "optimal streaming and tracking distinct elements with high probability":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weighted maximum independent set of geometric objects in turnstile streams")
  Member "faster 64 bit universal hashing using carry less multiplications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "parsing gigabytes of json per second")
  Member "pseudorandomness via the discrete fourier transform":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fourier bounds and pseudorandom generators for product tests")
  Member "high speed hashing for integers and strings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "peeling close to the orientability threshold spatial coupling in hashing based data structures")
  Member "from independence to expansion and back again":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "algorithms for similarity search and pseudorandomness")
  Member "generating k independent variables in constant time":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "algorithms for similarity search and pseudorandomness")
  Member "hashing for statistics over k partitions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fully understanding the hashing trick")
  Member "bottom k and priority sampling set similarity and subset sums with minimal independence":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "algorithms for similarity search and pseudorandomness")

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)