human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "learning imbalanced datasets with label distribution aware margin loss":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning from multiple experts self paced knowledge distillation for long tailed classification")
  Member "two models of double descent for weak features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a deep conditioning treatment of neural networks")
  Member "harmless interpolation of noisy data in regression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rethinking parameter counting in deep models effective dimensionality revisited")
  Member "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "large scale fine grained categorization and domain specific transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rethinking the hyperparameters for fine tuning")
  Member "towards demystifying membership inference attacks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "data and model dependencies of membership inference attack")
  Member "dnn or k nn that is the generalize vs memorize question":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "detecting adversarial samples using influence functions and nearest neighbors")
  Member "privacy preserving prediction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "privgen preserving privacy of sequences through data generation")

Question: Please tell me which class the source paper 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)