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: "etalumis bringing probabilistic programming to scientific simulators at scale"
  Abstract: Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models. However, applications to science remain limited because of the impracticability of rewriting complex scientific simulators in a PPL, the computational cost of inference, and the lack of scalable implementations. To address these, we present a novel PPL framework that couples directly to existing scientific simulators through a cross-platform probabilistic execution protocol and provides Markov chain Monte Carlo (MCMC) and deep-learning-based inference compilation (IC) engines for tractable inference. To guide IC inference, we perform distributed training of a dynamic 3DCNN--LSTM architecture with a PyTorch-MPI-based framework on 1,024 32-core CPU nodes of the Cori supercomputer with a global minibatch size of 128k: achieving a performance of 450 Tflop/s through enhancements to PyTorch. We demonstrate a Large Hadron Collider (LHC) use-case with the C++ Sherpa simulator and achieve the largest-scale posterior inference in a Turing-complete PPL.

Connected through hyperedge HE-1 (induced by source paper "approximate inference in discrete distributions with monte carlo tree search and value functions"):
  with members:
    - "meta learning mcmc proposals"
    - "relational inductive biases deep learning and graph networks"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "equivalence between policy gradients and soft q learning"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "variational inference for monte carlo objectives"
    - "gradient estimation using stochastic computation graphs"
    - "neural adaptive sequential monte carlo"

Connected through hyperedge HE-2 (induced by source paper "deep probabilistic surrogate networks for universal simulator approximation"):
  with members:
    - "pyro deep universal probabilistic programming"
    - "efficient probabilistic inference in the quest for physics beyond the standard model"
    - "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:
    - "efficient probabilistic inference in the quest for physics beyond the standard model"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "efficient probabilistic inference in the quest for physics beyond the standard model"):
  with members:
    - "amortized rejection sampling in universal probabilistic programming"
    - "pyro deep universal probabilistic programming"
    - "improvements to inference compilation for probabilistic programming in large scale scientific simulators"
    - "neural autoregressive distribution estimation"
    - "inference compilation and universal probabilistic programming"
    - "tensorflow a system for large scale machine learning"
    - "automatic differentiation in machine learning a survey"
    - "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)