human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep unsupervised learning using nonequilibrium thermodynamics"):
  Abstract: A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data. This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model. We additionally release an open source reference implementation of the algorithm.

Includes members:
  Member "nice non linear independent components estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "reweighted wake sleep":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meta amortized variational inference and learning")
  Member "accurate and conservative estimates of mrf log likelihood using reverse annealing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "implicit generation and generalization in energy based models")
  Member "loopy belief propagation for approximate inference an empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "analyzing differentiable fuzzy logic operators")
  Member "deep generative stochastic networks trainable by backprop":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "density estimation and incremental learning of latent vector for generative autoencoders")
  Member "fast large scale optimization by unifying stochastic gradient and quasi newton methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "analysis of dawnbench a time to accuracy machine learning performance benchmark")
  Member "deep autoregressive networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "revisiting spatial invariance with low rank local connectivity")
  Member "better mixing via deep representations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "your gan is secretly an energy based model and you should use discriminator driven latent sampling")

Question: Please tell me which class the source paper 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)