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: "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
  Abstract: The convergence rate and final performance of common deep learning models have significantly benefited from heuristics such as learning rate schedules, knowledge distillation, skip connections, and normalization layers. In the absence of theoretical underpinnings, controlled experiments aimed at explaining these strategies can aid our understanding of deep learning landscapes and the training dynamics. Existing approaches for empirical analysis rely on tools of linear interpolation and visualizations with dimensionality reduction, each with their limitations. Instead, we revisit such analysis of heuristics through the lens of recently proposed methods for loss surface and representation analysis, viz., mode connectivity and canonical correlation analysis (CCA), and hypothesize reasons for the success of the heuristics. In particular, we explore knowledge distillation and learning rate heuristics of (cosine) restarts and warmup using mode connectivity and CCA. Our empirical analysis suggests that: (a) the reasons often quoted for the success of cosine annealing are not evidenced in practice; (b) that the effect of learning rate warmup is to prevent the deeper layers from creating training instability; and (c) that the latent knowledge shared by the teacher is primarily disbursed to the deeper layers.

Connected through hyperedge HE-1 (induced by source paper "disentangling adaptive gradient methods from learning rates"):
  with members:
    - "the step decay schedule a near optimal geometrically decaying learning rate procedure for least squares"
    - "extreme tensoring for low memory preconditioning"
    - "on empirical comparisons of optimizers for deep learning"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "escaping saddle points with adaptive gradient methods"
    - "an exponential learning rate schedule for deep learning"
    - "deepobs a deep learning optimizer benchmark suite"

Connected through hyperedge HE-2 (induced by source paper "large batch training does not need warmup"):
  with members:
    - "large batch training for lstm and beyond"
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "on lipschitz bounds of general convolutional neural networks"
    - "image classification at supercomputer scale"
    - "exploring the limits of weakly supervised pretraining"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "measuring the effects of data parallelism on neural network training"

Connected through hyperedge HE-3 (induced by source paper "self distillation amplifies regularization in hilbert space"):
  with members:
    - "hydra preserving ensemble diversity for model distillation"
    - "disentangling feature and lazy learning in deep neural networks an empirical study"
    - "relational knowledge distillation"
    - "variational information distillation for knowledge transfer"
    - "knowledge transfer with jacobian matching"
    - "knowledge distillation by on the fly native ensemble"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "distral robust multitask reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "budgeted training rethinking deep neural network training under resource constraints"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "learnable embedding space for efficient neural architecture compression"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "deep rigid instance scene flow"
    - "gradient descent finds global minima of deep neural networks"
    - "darts differentiable architecture search"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

Connected through hyperedge HE-5 (induced by source paper "utility analysis of network architectures for 3d point cloud processing"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a cnn annularly convolutional neural networks on point clouds"
    - "similarity of neural network representations revisited"
    - "quantifying layerwise information discarding of neural networks"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "3d point capsule networks"
    - "multiresolution tree networks for 3d point cloud processing"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "training provably robust models by polyhedral envelope regularization"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "towards stable and efficient training of verifiably robust neural networks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "on certifying non uniform bound against adversarial attacks"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "stochastic activation pruning for robust adversarial defense"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"

Connected through hyperedge HE-7 (induced by source paper "rapid learning or feature reuse towards understanding the effectiveness of maml"):
  with members:
    - "similarity of neural network representations revisited"
    - "meta learning with differentiable convex optimization"
    - "meta learning representations for continual learning"
    - "few shot text classification with distributional signatures"
    - "a closer look at few shot classification"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "transfusion understanding transfer learning for medical imaging"
    - "on first order meta learning algorithms"

Connected through hyperedge HE-8 (induced by source paper "on the variance of the adaptive learning rate and beyond"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "fixup initialization residual learning without normalization"
    - "on the convergence of adam and beyond"
    - "roberta a robustly optimized bert pretraining approach"
    - "training tips for the transformer model"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"

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)