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: "hyperminhash jaccard index sketching in loglog space"
  Abstract: In this extended abstract, we describe and analyse a streaming probabilistic sketch, HYPERMINHASH, to estimate the Jaccard index (or Jaccard similarity coefficient) over two sets $A$ and $B$. HyperMinHash can be thought of as a compression of standard MinHash by building off of a HyperLogLog count-distinct sketch. Given Jaccard index $\delta$, using $k$ buckets of size $O(\log(l) + \log\log(|A \cup B|))$ (in practice, typically 2 bytes) per set, HyperMinHash streams over $A$ and $B$ and generates an estimate of the Jaccard index $\delta$ with error $O(1/l + \sqrt{k/\delta})$. This improves on the best previously known sketch, MinHash, which requires the same number of storage units (buckets), but using $O(\log(|A \cup B|))$ bit per bucket. For instance, our new algorithm allows estimating Jaccard indices of 0.01 for set cardinalities on the order of $10^{19}$ with relative error of around 5% using 64KiB of memory; the previous state-of-the-art MinHash can only estimate Jaccard indices for cardinalities of $10^{10}$ with the same memory consumption. Alternately, one can think of HyperMinHash as an augmentation of b-bit MinHash that enables streaming updates, unions, and cardinality estimation (and thus intersection cardinality by way of Jaccard), while using $\log\log$ extra bits.

Connected through hyperedge HE-1 (induced by source paper "a memory efficient sketch method for estimating high similarities in streaming sets"):
  with members:
    - "bagminhash minwise hashing algorithm for weighted sets"
    - "optimal densification for fast and accurate minwise hashing"
    - "a minimal variance estimator for the cardinality of big data set intersection"
    - "consistent weighted sampling made fast small and easy"
    - "finding associations and computing similarity via biased pair sampling"

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)