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: "sobolev training for neural networks"
  Abstract: At the heart of deep learning we aim to use neural networks as function approximators - training them to produce outputs from inputs in emulation of a ground truth function or data creation process. In many cases we only have access to input-output pairs from the ground truth, however it is becoming more common to have access to derivatives of the target output with respect to the input - for example when the ground truth function is itself a neural network such as in network compression or distillation. Generally these target derivatives are not computed, or are ignored. This paper introduces Sobolev Training for neural networks, which is a method for incorporating these target derivatives in addition the to target values while training. By optimising neural networks to not only approximate the function's outputs but also the function's derivatives we encode additional information about the target function within the parameters of the neural network. Thereby we can improve the quality of our predictors, as well as the data-efficiency and generalization capabilities of our learned function approximation. We provide theoretical justifications for such an approach as well as examples of empirical evidence on three distinct domains: regression on classical optimisation datasets, distilling policies of an agent playing Atari, and on large-scale applications of synthetic gradients. In all three domains the use of Sobolev Training, employing target derivatives in addition to target values, results in models with higher accuracy and stronger generalisation.

Connected through hyperedge HE-1 (induced by source paper "understanding and improving knowledge distillation"):
  with members:
    - "billion scale semi supervised learning for image classification"
    - "when does label smoothing help"
    - "revisit knowledge distillation a teacher free framework"
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "bag of tricks for image classification with convolutional neural networks"
    - "knowledge transfer with jacobian matching"
    - "knowledge distillation with adversarial samples supporting decision boundary"
    - "large scale distributed neural network training through online distillation"

Connected through hyperedge HE-2 (induced by source paper "long short term sample distillation"):
  with members:
    - "snapshot distillation teacher student optimization in one generation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "on calibration of modern neural networks"
    - "snapshot ensembles train 1 get m for free"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "temporal ensembling for semi supervised learning"

Connected through hyperedge HE-3 (induced by source paper "credit assignment techniques in stochastic computation graphs"):
  with members:
    - "total stochastic gradient algorithms and applications in reinforcement learning"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "the mirage of action dependent baselines in reinforcement learning"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "implicit reparameterization gradients"
    - "learning and querying fast generative models for reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "teacher students knowledge distillation for siamese trackers"):
  with members:
    - "context aware deep feature compression for high speed visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "a twofold siamese network for real time object tracking"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "learning dynamic memory networks for object tracking"
    - "trackingnet a large scale dataset and benchmark for object tracking in the wild"
    - "end to end representation learning for correlation filter based tracking"
    - "deep mutual learning"

Connected through hyperedge HE-5 (induced by source paper "full gradient representation for neural network visualization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "knowledge transfer with jacobian matching"
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"
    - "knowledge transfer via distillation of activation boundaries formed by hidden neurons"
    - "sanity checks for saliency maps"
    - "evaluating feature importance estimates"
    - "smoothgrad removing noise by adding noise"
    - "explaining nonlinear classification decisions with deep taylor decomposition"

Connected through hyperedge HE-6 (induced by source paper "training machine learning models by regularizing their explanations"):
  with members:
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "certified defenses against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "smoothgrad removing noise by adding noise"
    - "network dissection quantifying interpretability of deep visual representations"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-7 (induced by source paper "mix match agent curricula for reinforcement learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "automated curriculum learning for neural networks"
    - "deep mutual learning"
    - "emergence of locomotion behaviours in rich environments"
    - "population based training of neural networks"
    - "distral robust multitask reinforcement learning"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "reinforcement learning with unsupervised auxiliary tasks"

Connected through hyperedge HE-8 (induced by source paper "local critic training of deep neural networks"):
  with members:
    - "multi scale dense networks for resource efficient image classification"
    - "identity mappings in deep residual networks"
    - "decoupled neural interfaces using synthetic gradients"
    - "training neural networks without gradients a scalable admm approach"
    - "fractalnet ultra deep neural networks without residuals"
    - "neural architecture search with reinforcement learning"
    - "adanet adaptive structural learning of artificial neural networks"
    - "deep residual learning for image 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)