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: "minimax rates of entropy estimation on large alphabets via best polynomial approximation"
  Abstract: Consider the problem of estimating the Shannon entropy of a distribution over $k$ elements from $n$ independent samples. We show that the minimax mean-square error is within universal multiplicative constant factors of $$\Big(\frac{k }{n \log k}\Big)^2 + \frac{\log^2 k}{n}$$ if $n$ exceeds a constant factor of $\frac{k}{\log k}$; otherwise there exists no consistent estimator. This refines the recent result of Valiant-Valiant \cite{VV11} that the minimal sample size for consistent entropy estimation scales according to $\Theta(\frac{k}{\log k})$. The apparatus of best polynomial approximation plays a key role in both the construction of optimal estimators and, via a duality argument, the minimax lower bound.

Connected through hyperedge HE-1 (induced by source paper "to split or not to split the impact of disparate treatment in classification"):
  with members:
    - "algorithmic fairness"
    - "recovering from biased data can fairness constraints improve accuracy"
    - "repairing without retraining avoiding disparate impact with counterfactual distributions"
    - "a framework for understanding unintended consequences of machine learning"
    - "optimized score transformation for fair classification"
    - "fairness with minimal harm a pareto optimal approach for healthcare"
    - "obfuscation via information density estimation"
    - "welfare and distributional impacts of fair classification"

Connected through hyperedge HE-2 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples"):
  with members:
    - "towards testing monotonicity of distributions over general posets"
    - "fast and sample near optimal algorithms for learning multidimensional histograms"
    - "on learning mixtures of well separated gaussians"
    - "efficient robust proper learning of log concave distributions"
    - "collision based testers are optimal for uniformity and closeness"
    - "testing bayesian networks"
    - "on the structure covering and learning of poisson multinomial distributions"
    - "optimal testing for properties of distributions"

Connected through hyperedge HE-3 (induced by source paper "minimax optimal estimation of kl divergence for continuous distributions"):
  with members:
    - "estimation of kl divergence optimal minimax rate"
    - "minimax rate optimal estimation of divergences between discrete distributions"
    - "demystifying fixed k nearest neighbor information estimators"
    - "universal outlying sequence detection for continuous observations"
    - "ensemble estimation of multivariate f divergence"

Connected through hyperedge HE-4 (induced by source paper "a general framework for symmetric property estimation"):
  with members:
    - "efficient profile maximum likelihood for universal symmetric property estimation"
    - "approximate profile maximum likelihood"
    - "estimating the unseen from multiple populations"
    - "a unified maximum likelihood approach for optimal distribution property estimation"
    - "minimax estimation of functionals of discrete distributions"

Connected through hyperedge HE-5 (induced by source paper "importance of small probability events in big data information measures applications and challenges"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "matching users preference under target revenue constraints in data recommendation systems"
    - "differential message importance measure a new approach to the required sampling number in big data structure characterization"
    - "decorrelation of neutral vector variables theory and applications"
    - "group cooperation with optimal resource allocation in wireless powered communication networks"
    - "amplifying inter message distance on information divergence measures in big data"
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "linear complexity exponentially consistent tests for universal outlying sequence detection"

Connected through hyperedge HE-6 (induced by source paper "obfuscation via information density estimation"):
  with members:
    - "privacy amplification by mixing and diffusion mechanisms"
    - "estimation efficiency under privacy constraints"
    - "distributed generation of privacy preserving data with user customization"
    - "on the robustness of information theoretic privacy measures and mechanisms"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "generative adversarial privacy"
    - "an operational approach to information leakage"
    - "a tunable measure for information leakage"

Connected through hyperedge HE-7 (induced by source paper "minimax rates of estimating approximate differential privacy"):
  with members:
    - "proving differential privacy with shadow execution"
    - "generative adversarial privacy"
    - "property testing for differential privacy"
    - "detecting violations of differential privacy"
    - "pacgan the power of two samples in generative adversarial networks"
    - "estimating mutual information for discrete continuous mixtures"
    - "collecting telemetry data privately"
    - "which distribution distances are sublinearly testable"

Connected through hyperedge HE-8 (induced by source paper "differentially private anonymized histograms"):
  with members:
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "inspectre privately estimating the unseen"
    - "efficient lipschitz extensions for high dimensional graph statistics and node private degree distributions"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "minimax estimation of functionals of discrete distributions"
    - "extremal mechanisms for local differential privacy"
    - "estimating renyi entropy of discrete distributions"
    - "on modeling profiles instead of values"

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.IT(Information Theory)