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: "transfer learning with neural automl"
  Abstract: We reduce the computational cost of Neural AutoML with transfer learning. AutoML relieves human effort by automating the design of ML algorithms. Neural AutoML has become popular for the design of deep learning architectures, however, this method has a high computation cost. To address this we propose Transfer Neural AutoML that uses knowledge from prior tasks to speed up network design. We extend RL-based architecture search methods to support parallel training on multiple tasks and then transfer the search strategy to new tasks. On language and image classification tasks, Transfer Neural AutoML reduces convergence time over single-task training by over an order of magnitude on many tasks.

Connected through hyperedge HE-1 (induced by source paper "meta learning of neural architectures for few shot learning"):
  with members:
    - "meta learning with warped gradient descent"
    - "searching for a robust neural architecture in four gpu hours"
    - "understanding and robustifying differentiable architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "autodispnet improving disparity estimation with automl"
    - "automated design of error resilient and hardware efficient deep neural networks"
    - "fast context adaptation via meta learning"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"

Connected through hyperedge HE-2 (induced by source paper "parameter efficient transfer learning for nlp"):
  with members:
    - "bert and pals projected attention layers for efficient adaptation in multi task learning"
    - "do better imagenet models transfer better"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "on self modulation for generative adversarial networks"
    - "modulating early visual processing by language"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-3 (induced by source paper "parsimonious deep learning a differential inclusion approach with global convergence"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "gradient descent finds global minima of deep neural networks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "msplit lbi realizing feature selection and dense estimation simultaneously in few shot and zero shot learning"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "a convergence theory for deep learning via over parameterization"
    - "soft filter pruning for accelerating deep convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "xfernas transfer neural architecture search"):
  with members:
    - "practical deep learning architecture optimization"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "tapas train less accuracy predictor for architecture search"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-5 (induced by source paper "training data distribution search with ensemble active learning"):
  with members:
    - "searching for a robust neural architecture in four gpu hours"
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "variational adversarial active learning"
    - "automl a survey of the state of the art"
    - "are all training examples created equal an empirical study"
    - "how transferable are the datasets collected by active learners"
    - "amc automl for model compression and acceleration on mobile devices"
    - "adaptive semantic segmentation with a strategic curriculum of proxy labels"

Connected through hyperedge HE-6 (induced by source paper "eat nas elastic architecture transfer for accelerating large scale neural architecture search"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "nsga net a multi objective genetic algorithm for neural architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "mnasnet platform aware neural architecture search for mobile"
    - "evolving deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "on the bounds of function approximations"):
  with members:
    - "size free generalization bounds for convolutional neural networks"
    - "evolving unsupervised deep neural networks for learning meaningful representations"
    - "amc automl for model compression and acceleration on mobile devices"
    - "auto keras an efficient neural architecture search system"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "large scale evolution of image classifiers"

Connected through hyperedge HE-8 (induced by source paper "transfer nas knowledge transfer between search spaces with transformer agents"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "nas bench 101 towards reproducible neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "neural program synthesis with priority queue training"
    - "darts differentiable architecture search"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural architecture search over a graph search space"
    - "learning transferable architectures for scalable 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)