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 "the thermodynamic variational objective"):
  Abstract: We introduce the thermodynamic variational objective (TVO) for learning in both continuous and discrete deep generative models. The TVO arises from a key connection between variational inference and thermodynamic integration that results in a tighter lower bound to the log marginal likelihood than the standard variational evidence lower bound (ELBO), while remaining as broadly applicable. We provide a computationally efficient gradient estimator for the TVO that applies to continuous, discrete, and non-reparameterizable distributions and show that the objective functions used in variational inference, variational autoencoders, wake sleep, and inference compilation are all special cases of the TVO. We evaluate the TVO for learning of discrete and continuous variational auto encoders, and find it achieves state of the art for learning in discrete variable models, and outperform VAEs on continuous variable models without using the reparameterization trick.

Includes members:
  Member "implicit reparameterization gradients":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial attacks on probabilistic autoregressive forecasting models")
  Member "doubly reparameterized gradient estimators for monte carlo objectives":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mvloc multimodal variational geometry aware learning for visual localization")
  Member "backpropagation through the void optimizing control variates for black box gradient estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalizable adversarial attacks with latent variable perturbation modelling")
  Member "rebar low variance unbiased gradient estimates for discrete latent variable models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inverse graphics gan learning to generate 3d shapes from unstructured 2d data")
  Member "filtering variational objectives":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relational state space model for stochastic multi object systems")
  Member "inference compilation and universal probabilistic programming":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning compositional rules via neural program synthesis")
  Member "the concrete distribution a continuous relaxation of discrete random variables":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "domain decluttering simplifying images to mitigate synthetic real domain shift and improve depth estimation")
  Member "variational inference for monte carlo objectives":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "estimating gradients for discrete random variables by sampling without replacement")

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)