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: "stochastic hyperparameter optimization through hypernetworks"
  Abstract: Machine learning models are usually tuned by nesting optimization of model weights inside the optimization of hyperparameters. We give a method to collapse this nested optimization into joint stochastic optimization of both weights and hyperparameters. Our method trains a neural network to output approximately optimal weights as a function of hyperparameters. We show that our method converges to locally optimal weights and hyperparameters for sufficiently large hypernets. We compare this method to standard hyperparameter optimization strategies and demonstrate its effectiveness for tuning thousands of hyperparameters.

Connected through hyperedge HE-1 (induced by source paper "optimizing millions of hyperparameters by implicit differentiation"):
  with members:
    - "dataset distillation"
    - "on solving minimax optimization locally a follow the ridge approach"
    - "meta learning with implicit gradients"
    - "deep equilibrium models"
    - "autoassist a framework to accelerate training of deep neural networks"
    - "generalized inner loop meta learning"
    - "self tuning networks bilevel optimization of hyperparameters using structured best response functions"
    - "adversarial learning of general transformations for data augmentation"

Connected through hyperedge HE-2 (induced by source paper "learning to impute a general framework for semi supervised learning"):
  with members:
    - "meta learning with implicit gradients"
    - "unsupervised data augmentation for consistency training"
    - "learning to learn via self critique"
    - "mixmatch a holistic approach to semi supervised learning"
    - "learning to self train for semi supervised few shot classification"
    - "self tuning networks bilevel optimization of hyperparameters using structured best response functions"
    - "semi supervised deep kernel learning regression with unlabeled data by minimizing predictive variance"
    - "meta learning for semi supervised few shot classification"

Connected through hyperedge HE-3 (induced by source paper "efficient optimization of loops and limits with randomized telescoping sums"):
  with members:
    - "credit assignment techniques in stochastic computation graphs"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "hypernetwork functional image representation"):
  with members:
    - "graph hypernetworks for neural architecture search"
    - "residual dense network for image super resolution"
    - "unsupervised image to image translation networks"
    - "enhanced deep residual networks for single image super resolution"
    - "smash one shot model architecture search through hypernetworks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "semantic image inpainting with deep generative models"

Connected through hyperedge HE-5 (induced by source paper "robust federated learning through representation matching and adaptive hyper parameters"):
  with members:
    - "reinforcement learning in non stationary environments"
    - "hyp rl hyperparameter optimization by reinforcement learning"
    - "robust and communication efficient federated learning from non iid data"
    - "federated learning strategies for improving communication efficiency"
    - "federated optimization for heterogeneous networks"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "federated learning with non iid data"
    - "reinforcement learning for learning rate control"

Connected through hyperedge HE-6 (induced by source paper "self tuning networks bilevel optimization of hyperparameters using structured best response functions"):
  with members:
    - "the numerics of gans"
    - "regularizing and optimizing lstm language models"
    - "population based training of neural networks"
    - "improved regularization of convolutional neural networks with cutout"
    - "smash one shot model architecture search through hypernetworks"
    - "unrolled generative adversarial networks"
    - "neural architecture search with reinforcement learning"
    - "scalable gradient based tuning of continuous regularization hyperparameters"

Connected through hyperedge HE-7 (induced by source paper "multi task hypernetworks"):
  with members:
    - "evolutionary neural hybrid agents for architecture search"
    - "residual dense network for image super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "neural architecture search with reinforcement learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "going deeper with convolutions"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-8 (induced by source paper "hypergan a generative model for diverse performant neural networks"):
  with members:
    - "adversarial distillation of bayesian neural network posteriors"
    - "dynamic filter networks"
    - "spatial transformer networks"
    - "the loss surfaces of multilayer networks"
    - "popular ensemble methods an empirical study"

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)