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: "streaming and sublinear approximation of entropy and information distances"
  Abstract: In many problems in data mining and machine learning, data items that need to be clustered or classified are not points in a high-dimensional space, but are distributions (points on a high dimensional simplex). For distributions, natural measures of distance are not the $\ell_p$ norms and variants, but information-theoretic measures like the Kullback-Leibler distance, the Hellinger distance, and others. Efficient estimation of these distances is a key component in algorithms for manipulating distributions. Thus, sublinear resource constraints, either in time (property testing) or space (streaming) are crucial. #R##N#We start by resolving two open questions regarding property testing of distributions. Firstly, we show a tight bound for estimating bounded, symmetric f-divergences between distributions in a general property testing (sublinear time) framework (the so-called combined oracle model). This yields optimal algorithms for estimating such well known distances as the Jensen-Shannon divergence and the Hellinger distance. Secondly, we close a $(\log n)/H$ gap between upper and lower bounds for estimating entropy $H$ in this model. In a stream setting (sublinear space), we give the first algorithm for estimating the entropy of a distribution. Our algorithm runs in polylogarithmic space and yields an asymptotic constant factor approximation scheme. We also provide other results along the space/time/approximation tradeoff curve.

Connected through hyperedge HE-1 (induced by source paper "uncertainty about uncertainty near optimal adaptive algorithms for estimating binary mixtures of unknown coins"):
  with members:
    - "maximum selection and ranking under noisy comparisons"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "learning populations of parameters"
    - "feeling the bern adaptive estimators for bernoulli probabilities of pairwise comparisons"
    - "a data quality metric dqm how to estimate the number of undetected errors in data sets"
    - "a polynomial lower bound for testing monotonicity"
    - "approval voting and incentives in crowdsourcing"
    - "simple robust and optimal ranking from pairwise comparisons"

Connected through hyperedge HE-2 (induced by source paper "anaconda a non adaptive conditional sampling algorithm for distribution testing"):
  with members:
    - "generalized uniformity testing"
    - "which distribution distances are sublinearly testable"
    - "a new approach for testing properties of discrete distributions"
    - "testing ising models"
    - "improving and extending the testing of distributions for shape restricted properties"
    - "faster sublinear algorithms using conditional sampling"
    - "optimal testing for properties of distributions"
    - "big data on the rise testing monotonicity of distributions"

Connected through hyperedge HE-3 (induced by source paper "pay for a sliding bloom filter and get counting distinct elements and entropy for free"):
  with members:
    - "constant time updates in hierarchical heavy hitters"
    - "randomized admission policy for efficient top k and frequency estimation"
    - "sliding bloom filters"

Connected through hyperedge HE-4 (induced by source paper "online facility location on semi random streams"):
  with members:
    - "new frameworks for offline and streaming coreset constructions"
    - "maximum matching in semi streaming with few passes"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-5 (induced by source paper "hpdedup a hybrid prioritized data deduplication mechanism for primary storage in the cloud"):
  with members:
    - "efficient hybrid inline and out of line deduplication for backup storage"

Connected through hyperedge HE-6 (induced by source paper "recovering structured probability matrices"):
  with members:
    - "a latent variable model approach to pmi based word embeddings"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "learning mixtures of gaussians in high dimensions"
    - "testing closeness with unequal sized samples"
    - "random walks on context spaces towards an explanation of the mysteries of semantic word embeddings"
    - "exact recovery in the stochastic block model"
    - "spectral redemption in clustering sparse networks"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "testing shape restrictions of discrete distributions"):
  with members:
    - "a new approach for testing properties of discrete distributions"
    - "improving and extending the testing of distributions for shape restricted properties"
    - "efficient robust proper learning of log concave distributions"
    - "optimal testing for properties of distributions"
    - "optimal learning via the fourier transform for sums of independent integer random variables"
    - "sample optimal density estimation in nearly linear time"
    - "optimal algorithms and lower bounds for testing closeness of structured distributions"
    - "testing poisson binomial distributions"

Connected through hyperedge HE-8 (induced by source paper "big data on the rise testing monotonicity of distributions"):
  with members:
    - "testing identity of structured distributions"
    - "testing probability distributions using conditional samples"
    - "on the power of conditional samples in distribution testing"
    - "learning k modal distributions via testing"
    - "testing k modal distributions optimal algorithms via reductions"

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)