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: "approximate profile maximum likelihood"
  Abstract: We propose an efficient algorithm for approximate computation of the profile maximum likelihood (PML), a variant of maximum likelihood maximizing the probability of observing a sufficient statistic rather than the empirical sample. The PML has appealing theoretical properties, but is difficult to compute exactly. Inspired by observations gleaned from exactly solvable cases, we look for an approximate PML solution, which, intuitively, clumps comparably frequent symbols into one symbol. This amounts to lower-bounding a certain matrix permanent by summing over a subgroup of the symmetric group rather than the whole group during the computation. We extensively experiment with the approximate solution, and find the empirical performance of our approach is competitive and sometimes significantly better than state-of-the-art performance for various estimation problems.

Connected through hyperedge HE-1 (induced by source paper "a general framework for symmetric property estimation"):
  with members:
    - "efficient profile maximum likelihood for universal symmetric property estimation"
    - "estimating the unseen from multiple populations"
    - "a unified maximum likelihood approach for optimal distribution property estimation"
    - "minimax estimation of functionals of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Connected through hyperedge HE-2 (induced by source paper "efficient profile maximum likelihood for universal symmetric property estimation"):
  with members:
    - "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"
    - "on modeling profiles instead of values"

Connected through hyperedge HE-3 (induced by source paper "a tight analysis of bethe approximation for permanent"):
  with members:
    - "log concave polynomials i entropy and a deterministic approximation algorithm for counting bases of matroids"
    - "belief propagation bethe approximation and polynomials"
    - "approximating the permanent of a random matrix with vanishing mean"
    - "a generalization of permanent inequalities and applications in counting and optimization"
    - "nash social welfare matrix permanent and stable polynomials"
    - "the bethe permanent of a nonnegative matrix"
    - "approximating the permanent with fractional belief propagation"
    - "approximating the permanent with belief propagation"

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.LG(Machine Learning)