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: "transferring knowledge across learning processes"
  Abstract: In complex transfer learning scenarios new tasks might not be tightly linked to previous tasks. Approaches that transfer information contained only in the final parameters of a source model will therefore struggle. Instead, transfer learning at a higher level of abstraction is needed. We propose Leap, a framework that achieves this by transferring knowledge across learning processes. We associate each task with a manifold on which the training process travels from initialization to final parameters and construct a meta-learning objective that minimizes the expected length of this path. Our framework leverages only information obtained during training and can be computed on the fly at negligible cost. We demonstrate that our framework outperforms competing methods, both in meta-learning and transfer learning, on a set of computer vision tasks. Finally, we demonstrate that Leap can transfer knowledge across learning processes in demanding reinforcement learning environments (Atari) that involve millions of gradient steps.

Connected through hyperedge HE-1 (induced by source paper "explaining knowledge distillation by quantifying the knowledge"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "similarity of neural network representations revisited"
    - "quantifying layerwise information discarding of neural networks"
    - "stiffness a new perspective on generalization in neural networks"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "understanding training and generalization in deep learning by fourier analysis"
    - "i revnet deep invertible networks"
    - "network dissection quantifying interpretability of deep visual representations"

Connected through hyperedge HE-2 (induced by source paper "meta learning across meta tasks for few shot learning"):
  with members:
    - "cross domain few shot classification via learned feature wise transformation"
    - "learning what and where to transfer"
    - "charting the right manifold manifold mixup for few shot learning"
    - "infinite mixture prototypes for few shot learning"
    - "on minimum discrepancy estimation for deep domain adaptation"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "bridging theory and algorithm for domain adaptation"
    - "transductive episodic wise adaptive metric for few shot learning"

Connected through hyperedge HE-3 (induced by source paper "automated relational meta learning"):
  with members:
    - "few shot unsupervised image to image translation"
    - "learning from multiple cities a meta learning approach for spatial temporal prediction"
    - "tafe net task aware feature embeddings for low shot learning"
    - "metapred meta learning for clinical risk prediction with limited patient electronic health records"
    - "tapnet neural network augmented with task adaptive projection for few shot learning"
    - "hierarchically structured meta learning"
    - "on first order meta learning algorithms"
    - "toward multimodal model agnostic meta learning"

Connected through hyperedge HE-4 (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-5 (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-6 (induced by source paper "hierarchically structured meta learning"):
  with members:
    - "on first order meta learning algorithms"
    - "toward multimodal model agnostic meta learning"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "reptile a scalable metalearning algorithm"
    - "bayesian model agnostic meta learning"
    - "hierarchical graph representation learning with differentiable pooling"
    - "meta learning for low resource neural machine translation"
    - "optimization landscape and expressivity of deep cnns"

Connected through hyperedge HE-7 (induced by source paper "modular meta learning with shrinkage"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "optimizing millions of hyperparameters by implicit differentiation"
    - "meta learning with differentiable convex optimization"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "on first order meta learning algorithms"
    - "understanding short horizon bias in stochastic meta optimization"
    - "recasting gradient based meta learning as hierarchical bayes"

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)