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: "on modeling profiles instead of values"
  Abstract: We consider the problem of estimating the distribution underlying an observed sample of data. Instead of maximum likelihood, which maximizes the probability of the ob served values, we propose a different estimate, the high-profile distribution, which maximizes the probability of the observed profile the number of symbols appearing any given number of times. We determine the high-profile distribution of several data samples, establish some of its general properties, and show that when the number of distinct symbols observed is small compared to the data size, the high-profile and maximum-likelihood distributions are roughly the same, but when the number of symbols is large, the distributions differ, and high-profile better explains the data.

Connected through hyperedge HE-1 (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"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"
    - "estimating renyi entropy of discrete distributions"

Connected through hyperedge HE-2 (induced by source paper "efficient profile maximum likelihood for universal symmetric property estimation"):
  with members:
    - "approximate profile maximum likelihood"
    - "estimating the unseen from multiple populations"
    - "minimax estimation of kl divergence between discrete distributions"
    - "a unified maximum likelihood approach for optimal distribution property estimation"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "minimax estimation of functionals of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Connected through hyperedge HE-3 (induced by source paper "optimal estimation of change in a population of parameters"):
  with members:
    - "concentration inequalities for the empirical distribution"
    - "learning populations of parameters"
    - "learning arbitrary statistical mixtures of discrete distributions"
    - "minimax estimation of functionals of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Connected through hyperedge HE-4 (induced by source paper "unified sample optimal property estimation in near linear time"):
  with members:
    - "efficient profile maximum likelihood for universal symmetric property estimation"
    - "inspectre privately estimating the unseen"
    - "minimax estimation of functionals of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Connected through hyperedge HE-5 (induced by source paper "phase transitions for the uniform distribution in the pml problem and its bethe approximation"):
  with members:
    - "the bethe permanent of a nonnegative matrix"
    - "counting in graph covers a combinatorial characterization of the bethe entropy function"
    - "approximating the permanent with belief propagation"
    - "belief propagation and beyond for particle tracking"

Connected through hyperedge HE-6 (induced by source paper "estimating renyi entropy of discrete distributions"):
  with members:
    - "minimax estimation of functionals of discrete distributions"
    - "maximum likelihood estimation of functionals of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"
    - "a unified characterization of generalized information and certainty measures"
    - "multiple source adaptation and the renyi divergence"
    - "information theory of dna shotgun sequencing"
    - "testing closeness of discrete distributions"
    - "guessing revisited a large deviations approach"

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.AI(Artificial Intelligence)