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: "snapshot ensembles train 1 get m for free"
  Abstract: Ensembles of neural networks are known to be much more robust and accurate than individual networks. However, training multiple deep networks for model averaging is computationally expensive. In this paper, we propose a method to obtain the seemingly contradictory goal of ensembling multiple neural networks at no additional training cost. We achieve this goal by training a single neural network, converging to several local minima along its optimization path and saving the model parameters. To obtain repeated rapid convergence, we leverage recent work on cyclic learning rate schedules. The resulting technique, which we refer to as Snapshot Ensembling, is simple, yet surprisingly effective. We show in a series of experiments that our approach is compatible with diverse network architectures and learning tasks. It consistently yields lower error rates than state-of-the-art single models at no additional training cost, and compares favorably with traditional network ensembles. On CIFAR-10 and CIFAR-100 our DenseNet Snapshot Ensembles obtain error rates of 3.4% and 17.4% respectively.

Connected through hyperedge HE-1 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost"):
  with members:
    - "adaptative inference cost with convolutional neural mixture models"
    - "depth adaptive transformer"
    - "hydra an ensemble of convolutional neural networks for geospatial land classification"
    - "robust training with ensemble consensus"
    - "deep tree learning for zero shot face anti spoofing"
    - "improved techniques for training adaptive deep networks"
    - "searching for mobilenetv3"
    - "universally slimmable networks and improved training techniques"

Connected through hyperedge HE-2 (induced by source paper "artificial intelligence for prosthetics challenge solutions"):
  with members:
    - "crossnorm normalization for off policy td reinforcement learning"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "learning to run challenge synthesizing physiologically accurate motion using deep reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "distributed prioritized experience replay"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning to run challenge solutions adapting reinforcement learning methods for neuromusculoskeletal environments"
    - "distributed distributional deterministic policy gradients"

Connected through hyperedge HE-3 (induced by source paper "pointaugment an auto augmentation framework for point cloud classification"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "multi level semantic feature augmentation for one shot learning"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "densepoint learning densely contextual representation for efficient point cloud processing"
    - "efficient learning on point clouds with basis point sets"
    - "kpconv flexible and deformable convolution for point clouds"
    - "fast autoaugment"
    - "shellnet efficient point cloud convolutional neural networks using concentric shells statistics"

Connected through hyperedge HE-4 (induced by source paper "batchensemble an alternative approach to efficient ensemble and lifelong learning"):
  with members:
    - "analyzing the role of model uncertainty for electronic health records"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "evaluating scalable bayesian deep learning methods for robust computer vision"
    - "experience replay for continual learning"
    - "efficient lifelong learning with a gem"
    - "reinforced continual learning"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-5 (induced by source paper "task augmentation by rotating for meta learning"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "meta learning with latent embedding optimization"
    - "learning to support exploiting structure information in support sets for one shot learning"
    - "dropblock a regularization method for convolutional networks"
    - "meta transfer learning for few shot learning"
    - "meta learning with differentiable closed form solvers"
    - "dynamic few shot visual learning without forgetting"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-6 (induced by source paper "mothernets rapid deep ensemble learning"):
  with members:
    - "self training ensemble networks for zero shot image recognition"
    - "efficient architecture search by network transformation"
    - "less is more a comprehensive framework for the number of components of ensemble classifiers"
    - "deep networks with stochastic depth"
    - "stochastic multiple choice learning for training diverse deep ensembles"
    - "learning without forgetting"
    - "wide residual networks"
    - "swapout learning an ensemble of deep architectures"

Connected through hyperedge HE-7 (induced by source paper "dibs diversity inducing information bottleneck in model ensembles"):
  with members:
    - "mim mutual information machine"
    - "variational adversarial active learning"
    - "evaluating scalable bayesian deep learning methods for robust computer vision"
    - "learning not to learn training deep neural networks with biased data"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "attention based ensemble for deep metric learning"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "dilated residual networks"

Connected through hyperedge HE-8 (induced by source paper "how does bn increase collapsed neural network filters"):
  with members:
    - "adam induces implicit weight sparsity in rectifier neural networks"
    - "towards understanding regularization in batch normalization"
    - "on implicit filter level sparsity in convolutional neural networks"
    - "dynamic runtime feature map pruning"
    - "focal loss for dense object detection"
    - "exploring the regularity of sparse structure in convolutional neural networks"
    - "channel pruning for accelerating very deep neural networks"
    - "instance normalization the missing ingredient for fast stylization"

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)