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 "graphical model sketch"):
  Abstract: Structured high-cardinality data arises in many domains and poses a major challenge for both modeling and inference, which is beyond current graphical model frameworks. We view these data as a stream $(x^{(t)})_{t = 1}^n$ of $n$ observations from an unknown distribution $P$, where $x^{(t)} \in [M]^K$ is a $K$-dimensional vector and $M$ is the cardinality of its entries, which is very large. Suppose that the graphical model $\mathcal{G}$ of $P$ is known, and let $\bar{P}$ be the maximum-likelihood estimate (MLE) of $P$ from $(x^{(t)})_{t = 1}^n$ conditioned on $\mathcal{G}$. In this work, we design and analyze algorithms that approximate $\bar{P}$ with $\hat{P}$, such that $\hat{P}(x) \approx \bar{P}(x)$ for any $x \in [M]^K$ with a high probability, and crucially in the space independent of $M$. The key idea of our approximations is to use the structure of $\mathcal{G}$ and approximately estimate its factors by "sketches". The sketches hash high-cardinality variables using random projections. Our approximations are computationally and space efficient, being independent of $M$. Our error bounds are multiplicative and provably improve upon those of the count-min (CM) sketch, a state-of-the-art approach to estimating the frequency of values in a stream, in a class of naive Bayes models. We evaluate our algorithms on synthetic and real-world problems, and report an order of magnitude improvements over the CM sketch.

Includes members:
  Member "taming the curse of dimensionality discrete integration by hashing and optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards efficient discrete integration via adaptive quantile queries")
  Member "context specific independence in bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "abstracting probabilistic models a logical perspective")
  Member "simple and deterministic matrix sketching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "hokusai sketching streams in real time":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compressing gradient optimizers via count sketches")

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)