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 based estimators for kernel density in high dimensions"
  Abstract: Given a set of points $P\subset \mathbb{R}^{d}$ and a kernel $k$, the Kernel Density Estimate at a point $x\in\mathbb{R}^{d}$ is defined as $\mathrm{KDE}_{P}(x)=\frac{1}{|P|}\sum_{y\in P} k(x,y)$. We study the problem of designing a data structure that given a data set $P$ and a kernel function, returns *approximations to the kernel density* of a query point in *sublinear time*. We introduce a class of unbiased estimators for kernel density implemented through locality-sensitive hashing, and give general theorems bounding the variance of such estimators. These estimators give rise to efficient data structures for estimating the kernel density in high dimensions for a variety of commonly used kernels. Our work is the first to provide data-structures with theoretical guarantees that improve upon simple random sampling in high dimensions.

Connected through hyperedge HE-1 (induced by source paper "the gaussiansketch for almost relative error kernel distance"):
  with members:
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "locality sensitive hashing of curves"
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "is input sparsity time possible for kernel low rank approximation"
    - "optimality of the johnson lindenstrauss lemma"
    - "streaming kernel principal component analysis"
    - "fastmmd ensemble of circular discrepancy for efficient two sample test"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-2 (induced by source paper "race sub linear memory sketches for approximate near neighbor search on streaming data"):
  with members:
    - "approximate nearest neighbors in limited space"
    - "billion scale similarity search with gpus"
    - "when hashes met wedges a distributed algorithm for finding high similarity vectors"
    - "near optimal euclidean metric compression"
    - "practical and optimal lsh for angular distance"
    - "locality sensitive hashing without false negatives"
    - "beyond locality sensitive hashing"
    - "new constructions of rip matrices with fast multiplication and fewer rows"

Connected through hyperedge HE-3 (induced by source paper "fair near neighbor search independent range sampling in high dimensions"):
  with members:
    - "independent range sampling revisited again"
    - "approximate nearest neighbor search in high dimensions"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "sub linear privacy preserving near neighbor search with untrusted server on large scale datasets"
    - "equality of opportunity in supervised learning"
    - "sub linear privacy preserving search with untrusted server and semi honest parties"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "probabilistic polynomials and hamming nearest neighbors"

Connected through hyperedge HE-4 (induced by source paper "sub linear race sketches for approximate kernel density estimation on streaming data"):
  with members:
    - "discrepancy coresets and sketches in machine learning"
    - "race sub linear memory sketches for approximate near neighbor search on streaming data"
    - "multi resolution hashing for fast pairwise summations"
    - "privacy via the johnson lindenstrauss transform"
    - "estimating continuous distributions in bayesian classifiers"
    - "super samples from kernel herding"

Connected through hyperedge HE-5 (induced by source paper "multi resolution hashing for fast pairwise summations"):
  with members:
    - "hardness of approximate nearest neighbor search"
    - "data dependent coresets for compressing neural networks with applications to generalization bounds"
    - "on the fine grained complexity of empirical risk minimization kernel methods and neural networks"
    - "stochastic optimization with bandit sampling"
    - "fast amortized inference and learning in log linear models with randomly perturbed nearest neighbor search"
    - "tight cell probe bounds for succinct boolean matrix vector multiplication"
    - "distance sensitive hashing"
    - "optimal hashing based time space trade offs for approximate near neighbors"

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)