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: "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.

Connected through hyperedge HE-1 (induced by source paper "learning graphical models from a distributed stream"):
  with members:
    - "communication optimal distributed clustering"
    - "benchmarking distributed stream processing platforms for iot applications"
    - "tensorflow a system for large scale machine learning"
    - "mllib machine learning in apache spark"
    - "distributed learning communication complexity and privacy"
    - "randomized algorithms for tracking distributed count frequencies and ranks"
    - "lower bounds for number in hand multiparty communication complexity made easy"

Connected through hyperedge HE-2 (induced by source paper "finding subcube heavy hitters in analytics data streams"):
  with members:
    - "testing ising models"
    - "space lower bounds for itemset frequency sketches"
    - "measuring independence of datasets"
    - "learning to filter spam e mail a comparison of a naive bayesian and a memory based approach"

Connected through hyperedge HE-3 (induced by source paper "finding subcube heavy hitters in data streams"):
  with members:
    - "space lower bounds for itemset frequency sketches"
    - "measuring independence of datasets"

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)