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: "credit assignment techniques in stochastic computation graphs"
  Abstract: Stochastic computation graphs (SCGs) provide a formalism to represent structured optimization problems arising in artificial intelligence, including supervised, unsupervised, and reinforcement learning. Previous work has shown that an unbiased estimator of the gradient of the expected loss of SCGs can be derived from a single principle. However, this estimator often has high variance and requires a full model evaluation per data point, making this algorithm costly in large graphs. In this work, we address these problems by generalizing concepts from the reinforcement learning literature. We introduce the concepts of value functions, baselines and critics for arbitrary SCGs, and show how to use them to derive lower-variance gradient estimates from partial model evaluations, paving the way towards general and efficient credit assignment for gradient-based optimization. In doing so, we demonstrate how our results unify recent advances in the probabilistic inference and reinforcement learning literature.

Connected through hyperedge HE-1 (induced by source paper "a unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme"):
  with members:
    - "pipps flexible model based policy search robust to the curse of chaos"
    - "total stochastic gradient algorithms and applications in reinforcement learning"
    - "leveraging exploration in off policy algorithms via normalizing flows"
    - "new tricks for estimating gradients of expectations"
    - "the mirage of action dependent baselines in reinforcement learning"
    - "clipped action policy gradient"
    - "understanding and correcting pathologies in the training of learned optimizers"
    - "implicit reparameterization gradients"

Connected through hyperedge HE-2 (induced by source paper "sub policy adaptation for hierarchical reinforcement learning"):
  with members:
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "self supervised learning of image embedding for continuous control"
    - "hierarchical reinforcement learning with hindsight"
    - "hierarchical visuomotor control of humanoids"
    - "the mirage of action dependent baselines in reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "visual reinforcement learning with imagined goals"
    - "latent space policies for hierarchical reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "differentiable algorithm networks for composable robot learning"):
  with members:
    - "universal planning networks"
    - "residual policy learning"
    - "learning dexterous in hand manipulation"
    - "mpc inspired neural network policies for sequential decision making"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "learning to search with mctsnets"
    - "differentiable mpc for end to end planning and control"
    - "on evaluation of embodied navigation agents"

Connected through hyperedge HE-4 (induced by source paper "efficient optimization of loops and limits with randomized telescoping sums"):
  with members:
    - "learned optimizers that outperform on wall clock and validation loss"
    - "understanding short horizon bias in stochastic meta optimization"
    - "understanding and correcting pathologies in the training of learned optimizers"
    - "truncated back propagation for bilevel optimization"
    - "unbiasing truncated backpropagation through time"
    - "an analysis of neural language modeling at multiple scales"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "stochastic hyperparameter optimization through hypernetworks"

Connected through hyperedge HE-5 (induced by source paper "value driven hindsight modelling"):
  with members:
    - "mastering atari go chess and shogi by planning with a learned model"
    - "deepmdp learning continuous latent space models for representation learning"
    - "an investigation of model free planning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "woulda coulda shoulda counterfactually guided policy search"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "value prediction network"
    - "asymmetric actor critic for image based robot learning"

Connected through hyperedge HE-6 (induced by source paper "loaded dice trading off bias and variance in any order score function estimators for reinforcement learning"):
  with members:
    - "some considerations on learning to explore via meta reinforcement learning"
    - "an introduction to deep reinforcement learning"
    - "promp proximal meta policy search"
    - "dice the infinitely differentiable monte carlo estimator"
    - "counterfactual multi agent policy gradients"
    - "learning with opponent learning awareness"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "high dimensional continuous control using generalized advantage estimation"

Connected through hyperedge HE-7 (induced by source paper "hindsight credit assignment"):
  with members:
    - "optimizing agent behavior over long time scales by transporting value"
    - "representation learning with contrastive predictive coding"
    - "hindsight experience replay"
    - "model based planning with discrete and continuous actions"
    - "variational intrinsic control"
    - "gradient estimation using stochastic computation graphs"
    - "trust region policy optimization"
    - "learning continuous control policies by stochastic value gradients"

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)