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: "learning internal representations"
  Abstract: Probably the most important problem in machine learning is the preliminary biasing of a learner's hypothesis space so that it is small enough to ensure good generalisation from reasonable training sets, yet large enough that it contains a good solution to the problem being learnt. In this paper a mechanism for {\em automatically} learning or biasing the learner's hypothesis space is introduced. It works by first learning an appropriate {\em internal representation} for a learning environment and then using that representation to bias the learner's hypothesis space for the learning of future tasks drawn from the same environment. #R##N#An internal representation must be learnt by sampling from {\em many similar tasks}, not just a single task as occurs in ordinary machine learning. It is proved that the number of examples $m$ {\em per task} required to ensure good generalisation from a representation learner obeys $m = O(a+b/n)$ where $n$ is the number of tasks being learnt and $a$ and $b$ are constants. If the tasks are learnt independently ({\em i.e.} without a common representation) then $m=O(a+b)$. It is argued that for learning environments such as speech and character recognition $b\gg a$ and hence representation learning in these environments can potentially yield a drastic reduction in the number of examples required per task. It is also proved that if $n = O(b)$ (with $m=O(a+b/n)$) then the representation learnt will be good for learning novel tasks from the same environment, and that the number of examples required to generalise well on a novel task will be reduced to $O(a)$ (as opposed to $O(a+b)$ if no representation is used). #R##N#It is shown that gradient descent can be used to train neural network representations and experiment results are reported providing strong qualitative support for the theoretical results.

Connected through hyperedge HE-1 (induced by source paper "automated machine learning state of the art and open challenges"):
  with members:
    - "meta learning a survey"
    - "autostacker a compositional evolutionary learning system"
    - "regularized evolution for image classifier architecture search"
    - "rafiki machine learning as an analytics service system"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "nsga net a multi objective genetic algorithm for neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "a baseline for few shot image classification"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "a closer look at few shot classification"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "few shot learning with embedded class models and shot free meta training"
    - "on first order meta learning algorithms"
    - "meta learning for semi supervised few shot classification"
    - "bag of tricks for image classification with convolutional neural networks"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-3 (induced by source paper "meta q learning"):
  with members:
    - "a baseline for few shot image classification"
    - "p3o policy on policy off policy optimization"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "on first order meta learning algorithms"
    - "off policy deep reinforcement learning without exploration"
    - "addressing function approximation error in actor critic methods"
    - "evolved policy gradients"
    - "are deep policy gradient algorithms truly policy gradient algorithms"

Connected through hyperedge HE-4 (induced by source paper "transformative machine learning"):
  with members:
    - "meta learning a survey"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "meta qsar a large scale application of meta learning to drug design and discovery"
    - "how transferable are features in deep neural networks"
    - "openml networked science in machine learning"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "clustered multi task learning a convex formulation"

Connected through hyperedge HE-5 (induced by source paper "multi label robust factorization autoencoder and its application in predicting drug drug interactions"):
  with members:
    - "scalable and effective deep cca via soft decorrelation"
    - "discovering hidden factors of variation in deep networks"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-6 (induced by source paper "axnet approximate computing using an end to end trainable neural network"):
  with members:
    - "nips 2016 tutorial generative adversarial networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "class imbalance problem in data mining review"

Connected through hyperedge HE-7 (induced by source paper "far ho a bilevel programming package for hyperparameter optimization and meta learning"):
  with members:
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-8 (induced by source paper "lifelong learning of discriminative representations"):
  with members:
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "learning task grouping and overlap in multi task learning"
    - "domain adaptation for statistical classifiers"
    - "a model of inductive bias learning"

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)