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 to teach"
  Abstract: Teaching plays a very important role in our society, by spreading human knowledge and educating our next generations. A good teacher will select appropriate teaching materials, impact suitable methodologies, and set up targeted examinations, according to the learning behaviors of the students. In the field of artificial intelligence, however, one has not fully explored the role of teaching, and pays most attention to machine \emph{learning}. In this paper, we argue that equal attention, if not more, should be paid to teaching, and furthermore, an optimization framework (instead of heuristics) should be used to obtain good teaching strategies. We call this approach `learning to teach'. In the approach, two intelligent agents interact with each other: a student model (which corresponds to the learner in traditional machine learning algorithms), and a teacher model (which determines the appropriate data, loss function, and hypothesis space to facilitate the training of the student model). The teacher model leverages the feedback from the student model to optimize its own teaching strategies by means of reinforcement learning, so as to achieve teacher-student co-evolution. To demonstrate the practical value of our proposed approach, we take the training of deep neural networks (DNN) as an example, and show that by using the learning to teach techniques, we are able to use much less training data and fewer iterations to achieve almost the same accuracy for different kinds of DNN models (e.g., multi-layer perceptron, convolutional neural networks and recurrent neural networks) under various machine learning tasks (e.g., image classification and text understanding).

Connected through hyperedge HE-1 (induced by source paper "generative teaching networks accelerating neural architecture search by learning to generate synthetic training data"):
  with members:
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "learned optimizers that outperform on wall clock and validation loss"
    - "learning dexterous in hand manipulation"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "meta learning update rules for unsupervised representation learning"
    - "an empirical study of example forgetting during deep neural network learning"

Connected through hyperedge HE-2 (induced by source paper "rethinking curriculum learning with incremental labels and adaptive compensation"):
  with members:
    - "on the power of curriculum learning in training deep networks"
    - "residual networks of residual networks multilevel residual networks"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "experience replay for continual learning"
    - "shakedrop regularization for deep residual learning"
    - "drop activation implicit parameter reduction and harmonic regularization"
    - "label refinery improving imagenet classification through label progression"
    - "end to end incremental learning"

Connected through hyperedge HE-3 (induced by source paper "open world learning and application to product classification"):
  with members:
    - "lifelong domain word embedding via meta learning"
    - "efficient large scale domain classification with personalized attention"
    - "probabilistic model agnostic meta learning"
    - "doc deep open classification of text documents"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-4 (induced by source paper "on the power of curriculum learning in training deep networks"):
  with members:
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "automated curriculum learning for neural networks"
    - "reverse curriculum generation for reinforcement learning"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "cyclical learning rates for training neural networks"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-5 (induced by source paper "mutual reinforcement learning"):
  with members:
    - "the assistive multi armed bandit"
    - "cooperative inverse reinforcement learning"
    - "multi armed bandits for intelligent tutoring systems"

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)