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: "dice the infinitely differentiable monte carlo estimator"
  Abstract: The score function estimator is widely used for estimating gradients of stochastic objectives in Stochastic Computation Graphs (SCG), eg. in reinforcement learning and meta-learning. While deriving the first-order gradient estimators by differentiating a surrogate loss (SL) objective is computationally and conceptually simple, using the same approach for higher-order gradients is more challenging. Firstly, analytically deriving and implementing such estimators is laborious and not compliant with automatic differentiation. Secondly, repeatedly applying SL to construct new objectives for each order gradient involves increasingly cumbersome graph manipulations. Lastly, to match the first-order gradient under differentiation, SL treats part of the cost as a fixed sample, which we show leads to missing and wrong terms for higher-order gradient estimators. To address all these shortcomings in a unified way, we introduce DiCE, which provides a single objective that can be differentiated repeatedly, generating correct gradient estimators of any order in SCGs. Unlike SL, DiCE relies on automatic differentiation for performing the requisite graph manipulations. We verify the correctness of DiCE both through a proof and through numerical evaluation of the DiCE gradient estimates. We also use DiCE to propose and evaluate a novel approach for multi-agent learning. Our code is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "multi step model agnostic meta learning convergence and improved algorithms"):
  with members:
    - "provably convergent policy gradient methods for model agnostic meta reinforcement learning"
    - "distribution agnostic model agnostic meta learning"
    - "es maml simple hessian free meta learning"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "online meta learning"
    - "meta learning with implicit gradients"
    - "on the convergence theory of gradient based model agnostic meta learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "decoupling adaptation from modeling with meta optimizers for meta learning"):
  with members:
    - "meta curvature"
    - "meta descent for online continual prediction"
    - "meta learning with warped gradient descent"
    - "alpha maml adaptive model agnostic meta learning"
    - "using learned optimizers to make models robust to input noise"
    - "meta learning via learned loss"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "on the convergence theory of gradient based model agnostic meta learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "mame model agnostic meta exploration"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "self supervised learning of image embedding for continuous control"
    - "norml no reward meta learning"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "on first order meta learning algorithms"
    - "meta reinforcement learning of structured exploration strategies"
    - "a study on overfitting in deep reinforcement learning"
    - "learning to learn how to learn self adaptive visual navigation using meta learning"

Connected through hyperedge HE-4 (induced by source paper "probabilistic models with deep neural networks"):
  with members:
    - "inferpy probabilistic modeling with deep neural networks made easy"
    - "pyro deep universal probabilistic programming"
    - "implicit reparameterization gradients"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "bayesian models of data streams with hierarchical power priors"

Connected through hyperedge HE-5 (induced by source paper "es maml simple hessian free meta learning"):
  with members:
    - "provably robust blackbox optimization for reinforcement learning"
    - "alpha maml adaptive model agnostic meta learning"
    - "norml no reward meta learning"
    - "evolvability es scalable and direct optimization of evolvability"
    - "structured monte carlo sampling for nonisotropic distributions via determinantal point processes"
    - "deep neuroevolution of recurrent and discrete world models"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "simple random search provides a competitive approach to reinforcement 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:
    - "credit assignment techniques in stochastic computation graphs"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "an introduction to deep reinforcement learning"
    - "promp proximal meta policy search"
    - "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 "new tricks for estimating gradients of expectations"):
  with members:
    - "using large ensembles of control variates for variational inference"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "muprop unbiased backpropagation for stochastic neural networks"

Connected through hyperedge HE-8 (induced by source paper "virel a variational inference framework for reinforcement learning"):
  with members:
    - "a theoretical analysis of deep q learning"
    - "a theory of regularized markov decision processes"
    - "maven multi agent variational exploration"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "fourier policy gradients"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "equivalence between policy gradients and soft q learning"

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)