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: "efficient probabilistic inference in the quest for physics beyond the standard model"
  Abstract: We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way. The execution of existing simulators as probabilistic programs enables highly interpretable posterior inference in the structured model defined by the simulator code base. We demonstrate the technique in particle physics, on a scientifically accurate simulation of the tau lepton decay, which is a key ingredient in establishing the properties of the Higgs boson. Inference efficiency is achieved via inference compilation where a deep recurrent neural network is trained to parameterize proposal distributions and control the stochastic simulator in a sequential importance sampling scheme, at a fraction of the computational cost of a Markov chain Monte Carlo baseline.

Connected through hyperedge HE-1 (induced by source paper "adaptive divergence for rapid adversarial optimization"):
  with members:
    - "darts differentiable architecture search"
    - "chaingan a sequential approach to gans"
    - "on convergence and stability of gans"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "which training methods for gans do actually converge"
    - "catboost unbiased boosting with categorical features"
    - "progressive growing of gans for improved quality stability and variation"
    - "stabilizing training of generative adversarial networks through regularization"

Connected through hyperedge HE-2 (induced by source paper "deep probabilistic surrogate networks for universal simulator approximation"):
  with members:
    - "etalumis bringing probabilistic programming to scientific simulators at scale"
    - "pyro deep universal probabilistic programming"
    - "on unifying deep generative models"
    - "neural message passing for quantum chemistry"
    - "accelerating eulerian fluid simulation with convolutional networks"
    - "inference compilation and universal probabilistic programming"
    - "neural programmer interpreters"
    - "venture a higher order probabilistic programming platform with programmable inference"

Connected through hyperedge HE-3 (induced by source paper "amortized rejection sampling in universal probabilistic programming"):
  with members:
    - "etalumis bringing probabilistic programming to scientific simulators at scale"
    - "denotational validation of higher order bayesian inference"
    - "deep amortized inference for probabilistic programs"
    - "a compilation target for probabilistic programming languages"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "church a language for generative models"

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)