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: "does learning require memorization a short tale about a long tail"
  Abstract: State-of-the-art results on image recognition tasks are achieved using over-parameterized learning algorithms that (nearly) perfectly fit the training set. This phenomenon is referred to as data interpolation or, informally, as memorization of the training data. The question of why such algorithms generalize well to unseen data is not adequately addressed by the standard theoretical frameworks and, as a result, significant theoretical and experimental effort has been devoted to understanding the properties of such algorithms. #R##N#We provide a simple model for prediction problems in which interpolating the dataset is necessary for achieving close-to-optimal generalization error. The model is motivated and supported by the results of several recent empirical works. In our model, data is sampled from a mixture of subpopulations and the frequencies of these subpopulations are chosen from some prior. The model allows to quantify the effect of not fitting the training data on the generalization performance of the learned classifier and demonstrates that memorization is necessary whenever frequencies are long-tailed. Image and text data are known to follow such distributions and therefore our results establish a formal link between these empirical phenomena. To the best of our knowledge, this is the first general framework that demonstrates statistical benefits of plain memorization for learning. Our results also have concrete implications for the cost of ensuring differential privacy in learning.

Connected through hyperedge HE-1 (induced by source paper "finding online neural update rules by learning to remember"):
  with members:
    - "backpropamine training self modifying neural networks with differentiable neuromodulated plasticity"
    - "dota 2 with large scale deep reinforcement learning"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "large scale adversarial representation learning"
    - "transformer xl attentive language models beyond a fixed length context"
    - "metalearned neural memory"
    - "network of evolvable neural units evolving to learn at a synaptic level"
    - "shaping belief states with generative environment models for rl"

Connected through hyperedge HE-2 (induced by source paper "exploring the memorization generalization continuum in deep learning"):
  with members:
    - "two models of double descent for weak features"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "robust anomaly detection in images using adversarial autoencoders"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "an empirical study of example forgetting during deep neural network learning"
    - "improving generalization performance by switching from adam to sgd"

Connected through hyperedge HE-3 (induced by source paper "diversity sampling is an implicit regularization for kernel methods"):
  with members:
    - "hidden stratification causes clinically meaningful failures in machine learning for medical imaging"
    - "scalable kernel k means clustering with nystrom approximation relative error bounds"
    - "sharp analysis of low rank kernel matrix approximations"

Connected through hyperedge HE-4 (induced by source paper "extracting robust and accurate features via a robust information bottleneck"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "unifying the brascamp lieb inequality and the entropy power inequality"
    - "where is the information in a deep neural network"
    - "a family of bayesian cram er rao bounds and consequences for log concave priors"
    - "theoretically principled trade off between robustness and accuracy"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "defending against whitebox adversarial attacks via randomized discretization"
    - "a spectral view of adversarially robust features"

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)