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: "meta learning update rules for unsupervised representation learning"
  Abstract: A major goal of unsupervised learning is to discover data representations that are useful for subsequent tasks, without access to supervised labels during training. Typically, this involves minimizing a surrogate objective, such as the negative log likelihood of a generative model, with the hope that representations useful for subsequent tasks will arise as a side effect. In this work, we propose instead to directly target later desired tasks by meta-learning an unsupervised learning rule which leads to representations useful for those tasks. Specifically, we target semi-supervised classification performance, and we meta-learn an algorithm -- an unsupervised weight update rule -- that produces representations useful for this task. Additionally, we constrain our unsupervised update rule to a be a biologically-motivated, neuron-local function, which enables it to generalize to different neural network architectures, datasets, and data modalities. We show that the meta-learned update rule produces useful features and sometimes outperforms existing unsupervised learning techniques. We further show that the meta-learned unsupervised update rule generalizes to train networks with different widths, depths, and nonlinearities. It also generalizes to train on data with randomly permuted input dimensions and even generalizes from image datasets to a text task.

Connected through hyperedge HE-1 (induced by source paper "automl zero evolving machine learning algorithms from scratch"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "nas evaluation is frustratingly hard"
    - "towards modular and programmable architecture search"
    - "weight agnostic neural networks"
    - "exploring randomly wired neural networks for image recognition"
    - "the evolved transformer"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "few shot learning via saliency guided hallucination of samples"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "learning from adversarial features for few shot classification"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "meta learning with warped gradient descent"
    - "edge labeling graph neural network for few shot learning"

Connected through hyperedge HE-3 (induced by source paper "an ensemble of epoch wise empirical bayes for few shot learning"):
  with members:
    - "multi level semantic feature augmentation for one shot learning"
    - "meta learning with differentiable convex optimization"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "lgm net learning to generate matching networks for few shot learning"
    - "finding task relevant features for few shot learning by category traversal"
    - "meta transfer learning through hard tasks"
    - "a closer look at few shot classification"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"

Connected through hyperedge HE-4 (induced by source paper "badger learning to learn learning algorithms through multi agent communication"):
  with members:
    - "backpropamine training self modifying neural networks with differentiable neuromodulated plasticity"
    - "efficient optimization of loops and limits with randomized telescoping sums"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "meta learning of sequential strategies"
    - "modularity as a means for complexity management in neural networks learning"
    - "a mathematical theory of cooperative communication"
    - "learning to communicate in multi agent reinforcement learning a review"
    - "mastering atari go chess and shogi by planning with a learned model"

Connected through hyperedge HE-5 (induced by source paper "meta learning with warped gradient descent"):
  with members:
    - "backpropamine training self modifying neural networks with differentiable neuromodulated plasticity"
    - "guided meta policy search"
    - "meta learning with differentiable convex optimization"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "meta learning representations for continual learning"
    - "on first order meta learning algorithms"
    - "understanding short horizon bias in stochastic meta optimization"
    - "recasting gradient based meta learning as hierarchical bayes"

Connected through hyperedge HE-6 (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"
    - "learning to teach"
    - "an empirical study of example forgetting during deep neural network learning"

Connected through hyperedge HE-7 (induced by source paper "learning to transfer unsupervised meta domain translation"):
  with members:
    - "optimal unsupervised domain translation"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "multimodal unsupervised image to image translation"
    - "unsupervised learning via meta learning"
    - "one shot unsupervised cross domain translation"
    - "conditional image to image translation"
    - "ganimation anatomically aware facial animation from a single image"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-8 (induced by source paper "few shot classification on unseen domains by learning disparate modulators"):
  with members:
    - "episodic training for domain generalization"
    - "a closer look at few shot classification"
    - "mxml mixture of meta learners for few shot classification"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "taskonomy disentangling task transfer learning"
    - "unsupervised learning via meta 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)