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 "exact weighted minwise hashing in constant time"):
  Abstract: Weighted minwise hashing (WMH) is one of the fundamental subroutine, required by many celebrated approximation algorithms, commonly adopted in industrial practice for large scale-search and learning. The resource bottleneck of the algorithms is the computation of multiple (typically a few hundreds to thousands) independent hashes of the data. The fastest hashing algorithm is by Ioffe \cite{Proc:Ioffe_ICDM10}, which requires one pass over the entire data vector, $O(d)$ ($d$ is the number of non-zeros), for computing one hash. However, the requirement of multiple hashes demands hundreds or thousands passes over the data. This is very costly for modern massive dataset. #R##N#In this work, we break this expensive barrier and show an expected constant amortized time algorithm which computes $k$ independent and unbiased WMH in time $O(k)$ instead of $O(dk)$ required by Ioffe's method. Moreover, our proposal only needs a few bits (5 - 9 bits) of storage per hash value compared to around $64$ bits required by the state-of-art-methodologies. Experimental evaluations, on real datasets, show that for computing 500 WMH, our proposal can be 60000x faster than the Ioffe's method without losing any accuracy. Our method is also around 100x faster than approximate heuristics capitalizing on the efficient "densified" one permutation hashing schemes \cite{Proc:OneHashLSH_ICML14}. Given the simplicity of our approach and its significant advantages, we hope that it will replace existing implementations in practice.

Includes members:
  Member "consistent weighted sampling made fast small and easy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "topic discovery in massive text corpora based on min hashing")
  Member "on the k independence required by linear probing and minwise independence":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secretary ranking with minimal inversions")

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)