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: "the information complexity of learning tasks their structure and their distance"
  Abstract: We introduce an asymmetric distance in the space of learning tasks, and a framework to compute their complexity. These concepts are foundational to the practice of transfer learning, ubiquitous in Deep Learning, whereby a parametric model is pre-trained for a task, and then used for another after fine-tuning. The framework we develop is intrinsically non-asymptotic, capturing the finite nature of the training dataset, yet it allows distinguishing learning from memorization. It encompasses, as special cases, classical notions from Kolmogorov complexity, Shannon, and Fisher Information. However, unlike some of those frameworks, it can be applied easily to large-scale models and real-world datasets. It is the first framework to explicitly account for the optimization scheme, which plays a crucial role in Deep Learning, in measuring complexity and information.

Connected through hyperedge HE-1 (induced by source paper "revisiting meta learning as supervised learning"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "task2vec task embedding for meta learning"
    - "a closer look at few shot classification"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "meta learning a survey"
    - "meta learning with latent embedding optimization"
    - "meta learning transferable active learning policies by deep reinforcement learning"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "dissecting non vacuous generalization bounds based on the mean field approximation"):
  with members:
    - "in defense of uniform convergence generalization via derandomization with an application to interpolating predictors"
    - "compression based bound for non compressed network unified generalization error analysis of large compressible deep neural network"
    - "hessian based analysis of sgd for deep nets dynamics and generalization"
    - "eigendamage structured pruning in the kronecker factored eigenbasis"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "limitations of the empirical fisher approximation"
    - "information theoretic local minima characterization and regularization"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"

Connected through hyperedge HE-3 (induced by source paper "transferability and hardness of supervised classification tasks"):
  with members:
    - "registration free face ssd single shot analysis of smiles facial attributes and affect in the wild"
    - "beyond sharing weights for deep domain adaptation"
    - "task2vec task embedding for meta learning"
    - "on measuring the iconicity of a face"
    - "toward understanding catastrophic forgetting in continual learning"
    - "regularized learning for domain adaptation under label shifts"
    - "meta learning with latent embedding optimization"
    - "a modulation module for multi task learning with applications in image retrieval"

Connected through hyperedge HE-4 (induced by source paper "better pac bayes bounds for deep neural networks using the loss curvature"):
  with members:
    - "eigendamage structured pruning in the kronecker factored eigenbasis"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "limitations of the empirical fisher approximation"
    - "information matrices and generalization"
    - "some limitations of norm based generalization bounds in deep neural networks"
    - "data dependent pac bayes priors via differential privacy"
    - "fixing variational bayes deterministic variational inference for bayesian neural networks"
    - "fisher rao metric geometry and complexity of neural networks"

Connected through hyperedge HE-5 (induced by source paper "invenio discovering hidden relationships between tasks domains using structured meta learning"):
  with members:
    - "self supervised generalisation with meta auxiliary learning"
    - "machine learning on biomedical images interactive learning transfer learning class imbalance and beyond"
    - "the imaterialist fashion attribute dataset"
    - "task2vec task embedding for meta learning"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "taskonomy disentangling task transfer learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "interpretable and pedagogical examples"

Connected through hyperedge HE-6 (induced by source paper "where is the information in a deep neural network"):
  with members:
    - "adaptive estimators show information compression in deep neural networks"
    - "estimating information flow in neural networks"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "emergence of invariance and disentanglement in deep representations"
    - "opening the black box of deep neural networks via information"
    - "train faster generalize better stability of stochastic gradient descent"
    - "new insights and perspectives on the natural gradient method"
    - "a pac bayesian tutorial with a dropout bound"

Connected through hyperedge HE-7 (induced by source paper "dynamics and reachability of learning tasks"):
  with members:
    - "task2vec task embedding for meta learning"
    - "taskonomy disentangling task transfer learning"
    - "emergence of invariance and disentanglement in deep representations"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "new insights and perspectives on the natural gradient method"
    - "a pac bayesian tutorial with a dropout bound"
    - "kolmogorov s structure functions and model selection"

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)