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: "learning to optimize neural nets"
  Abstract: Learning to Optimize is a recently proposed framework for learning optimization algorithms using reinforcement learning. In this paper, we explore learning an optimization algorithm for training shallow neural nets. Such high-dimensional stochastic optimization problems present interesting challenges for existing reinforcement learning algorithms. We develop an extension that is suited to learning optimization algorithms in this setting and demonstrate that the learned optimization algorithm consistently outperforms other known optimization algorithms even on unseen tasks and is robust to changes in stochasticity of gradients and the neural net architecture. More specifically, we show that an optimization algorithm trained with the proposed method on the problem of training a neural net on MNIST generalizes to the problems of training neural nets on the Toronto Faces Dataset, CIFAR-10 and CIFAR-100.

Connected through hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies"):
  with members:
    - "on empirical comparisons of optimizers for deep learning"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "on the variance of the adaptive learning rate and beyond"
    - "the visual task adaptation benchmark"
    - "using learned optimizers to make models robust to input noise"
    - "deepobs a deep learning optimizer benchmark suite"
    - "nas bench 101 towards reproducible neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "improving generalization in meta reinforcement learning using learned objectives"):
  with members:
    - "meta learning curiosity algorithms"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "meta learning via learned loss"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"
    - "meta gradient reinforcement learning"
    - "bayesian model agnostic meta learning"

Connected through hyperedge HE-3 (induced by source paper "metalgan a cluster based adaptive training for few shot adversarial colorization"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "on first order meta learning algorithms"
    - "a zero shot framework for sketch based image retrieval"
    - "texturegan controlling deep image synthesis with texture patches"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "learned optimizers that scale and generalize"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "unsupervised diverse colorization via generative adversarial networks"

Connected through hyperedge HE-4 (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-5 (induced by source paper "meta inverse reinforcement learning with probabilistic context variables"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "multi task maximum entropy inverse reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "learning a prior over intent via meta inverse reinforcement learning"
    - "directed info gail learning hierarchical policies from unsegmented demonstrations using directed information"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"

Connected through hyperedge HE-6 (induced by source paper "metalgan multi domain label less image synthesis using cgans and meta learning"):
  with members:
    - "figr few shot image generation with reptile"
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "metalgan a cluster based adaptive training for few shot adversarial colorization"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "dna gan learning disentangled representations from multi attribute images"
    - "attgan facial attribute editing by only changing what you want"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "learning to learn by zeroth order oracle"):
  with members:
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "zeroth order stochastic variance reduction for nonconvex optimization"
    - "guided evolutionary strategies augmenting random search with surrogate gradients"
    - "guided evolutionary strategies escaping the curse of dimensionality in random search"
    - "query efficient hard label black box attack an optimization based approach"
    - "learning gradient descent better generalization and longer horizons"
    - "learned optimizers that scale and generalize"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-8 (induced by source paper "meta learning mcmc proposals"):
  with members:
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "signal based bayesian seismic monitoring"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to learn by gradient descent by gradient descent"
    - "inference compilation and universal probabilistic programming"
    - "neurally guided procedural models amortized inference for procedural graphics programs using neural networks"
    - "importance weighted autoencoders"
    - "neural adaptive sequential monte carlo"

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)