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: "optimal densification for fast and accurate minwise hashing"
  Abstract: Minwise hashing is a fundamental and one of the most successful hashing algorithm in the literature. Recent advances based on the idea of densification~\cite{Proc:OneHashLSH_ICML14,Proc:Shrivastava_UAI14} have shown that it is possible to compute $k$ minwise hashes, of a vector with $d$ nonzeros, in mere $(d + k)$ computations, a significant improvement over the classical $O(dk)$. These advances have led to an algorithmic improvement in the query complexity of traditional indexing algorithms based on minwise hashing. Unfortunately, the variance of the current densification techniques is unnecessarily high, which leads to significantly poor accuracy compared to vanilla minwise hashing, especially when the data is sparse. In this paper, we provide a novel densification scheme which relies on carefully tailored 2-universal hashes. We show that the proposed scheme is variance-optimal, and without losing the runtime efficiency, it is significantly more accurate than existing densification techniques. As a result, we obtain a significantly efficient hashing scheme which has the same variance and collision probability as minwise hashing. Experimental evaluations on real sparse and high-dimensional datasets validate our claims. We believe that given the significant advantages, our method will replace minwise hashing implementations in practice.

Connected through hyperedge HE-1 (induced by source paper "algorithms for similarity search and pseudorandomness"):
  with members:
    - "hardness of approximate nearest neighbor search"
    - "fast locality sensitive hashing for approximate near neighbor search"
    - "fast similarity sketching"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "scalable and robust set similarity join"
    - "fast cross polytope locality sensitive hashing"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"

Connected through hyperedge HE-2 (induced by source paper "probminhash a class of locality sensitive hash algorithms for the probability jaccard similarity"):
  with members:
    - "fast random integer generation in an interval"
    - "bagminhash minwise hashing algorithm for weighted sets"
    - "maximally consistent sampling and the jaccard index of probability distributions"
    - "fast similarity sketching"
    - "superminhash a new minwise hashing algorithm for jaccard similarity estimation"
    - "b bit minwise hashing"

Connected through hyperedge HE-3 (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"
    - "hyperminhash jaccard index sketching in loglog space"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "efficient sketching algorithm for sparse binary data"):
  with members:
    - "a reliable effective terascale linear learning system"

Connected through hyperedge HE-5 (induced by source paper "bagminhash minwise hashing algorithm for weighted sets"):
  with members:
    - "topic modeling of public repositories at scale using names in source code"
    - "fast similarity sketching"
    - "improved consistent weighted sampling revisited"
    - "superminhash a new minwise hashing algorithm for jaccard similarity estimation"
    - "ssh sketch shingle hash for indexing massive scale time series"
    - "linearized gmm kernels and normalized random fourier features"
    - "consistent weighted sampling made fast small and easy"
    - "optimal discrete uniform generation from coin flips and applications"

Connected through hyperedge HE-6 (induced by source paper "fast locality sensitive hashing frameworks for approximate near neighbor search"):
  with members:
    - "fast locality sensitive hashing for approximate near neighbor search"
    - "fast similarity sketching"
    - "scalable and robust set similarity join"
    - "fast cross polytope locality sensitive hashing"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "practical and optimal lsh for angular distance"
    - "hashing for similarity search a survey"

Connected through hyperedge HE-7 (induced by source paper "fast similarity sketching"):
  with members:
    - "set similarity search beyond minhash"
    - "optimal data dependent hashing for approximate near neighbors"
    - "hashing for statistics over k partitions"
    - "bottom k and priority sampling set similarity and subset sums with minimal independence"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-8 (induced by source paper "superminhash a new minwise hashing algorithm for jaccard similarity estimation"):
  with members:
    - "fast similarity sketching"
    - "optimal discrete uniform generation from coin flips and applications"

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)