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: "self tuning networks bilevel optimization of hyperparameters using structured best response functions"
  Abstract: Hyperparameter optimization can be formulated as a bilevel optimization problem, where the optimal parameters on the training set depend on the hyperparameters. We aim to adapt regularization hyperparameters for neural networks by fitting compact approximations to the best-response function, which maps hyperparameters to optimal weights and biases. We show how to construct scalable best-response approximations for neural networks by modeling the best-response as a single network whose hidden units are gated conditionally on the regularizer. We justify this approximation by showing the exact best-response for a shallow linear network with L2-regularized Jacobian can be represented by a similar gating mechanism. We fit this model using a gradient-based hyperparameter optimization algorithm which alternates between approximating the best-response around the current hyperparameters and optimizing the hyperparameters using the approximate best-response function. Unlike other gradient-based approaches, we do not require differentiating the training loss with respect to the hyperparameters, allowing us to tune discrete hyperparameters, data augmentation hyperparameters, and dropout probabilities. Because the hyperparameters are adapted online, our approach discovers hyperparameter schedules that can outperform fixed hyperparameter values. Empirically, our approach outperforms competing hyperparameter optimization methods on large-scale deep learning problems. We call our networks, which update their own hyperparameters online during training, Self-Tuning Networks (STNs).

Connected through hyperedge HE-1 (induced by source paper "mnemonics training multi class incremental learning without forgetting"):
  with members:
    - "large scale incremental learning"
    - "pytorch an imperative style high performance deep learning library"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"
    - "meta transfer learning for few shot learning"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "deep generative dual memory network for continual learning"
    - "end to end incremental learning"
    - "a strategy for an uncompromising incremental learner"

Connected through hyperedge HE-2 (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"
    - "adversarial learning of general transformations for data augmentation"
    - "on first order meta learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "dbsn measuring uncertainty through bayesian learning of deep neural network structures"):
  with members:
    - "functional variational bayesian neural networks"
    - "single path one shot neural architecture search with uniform sampling"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "eigenvalue corrected noisy natural gradient"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"

Connected through hyperedge HE-4 (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"
    - "semi supervised deep kernel learning regression with unlabeled data by minimizing predictive variance"
    - "meta learning for semi supervised few shot classification"
    - "unsupervised learning via meta learning"

Connected through hyperedge HE-5 (induced by source paper "scheduling the learning rate via hypergradients new insights and a new algorithm"):
  with members:
    - "efficient optimization of loops and limits with randomized telescoping sums"
    - "understanding short horizon bias in stochastic meta optimization"
    - "online learning rate adaptation with hypergradient descent"
    - "learned optimizers that scale and generalize"
    - "learning to learn by gradient descent by gradient descent"
    - "sgdr stochastic gradient descent with restarts"
    - "coin betting and parameter free online learning"
    - "scalable gradient based tuning of continuous regularization hyperparameters"

Connected through hyperedge HE-6 (induced by source paper "learning effective loss functions efficiently"):
  with members:
    - "learning optimal linear regularizers"
    - "solving linear programs in the current matrix multiplication time"
    - "imagenet large scale visual recognition challenge"
    - "a survey of algorithms and analysis for adaptive online learning"
    - "decaf a deep convolutional activation feature for generic visual recognition"

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)