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 "exploring the memorization generalization continuum in deep learning"):
  Abstract: Human learners appreciate that some facts demand memorization whereas other facts support generalization. For example, English verbs have irregular cases that must be memorized (e.g., go->went) and regular cases that generalize well (e.g., kiss->kissed, miss->missed). Likewise, deep neural networks have the capacity to memorize rare or irregular forms but nonetheless generalize across instances that share common patterns or structures. We analyze how individual instances are treated by a model on the memorization-generalization continuum via a consistency score. The score is the expected accuracy of a particular architecture for a held-out instance on a training set of a fixed size sampled from the data distribution. We obtain empirical estimates of this score for individual instances in multiple datasets, and we show that the score identifies out-of-distribution and mislabeled examples at one end of the continuum and regular examples at the other end. We explore three proxies to the consistency score: kernel density estimation on input and hidden representations; and the time course of training, i.e., learning speed. In addition to helping to understand the memorization versus generalization dynamics during training, the C-score proxies have potential application for out-of-distribution detection, curriculum learning, and active data collection.

Includes members:
  Member "two models of double descent for weak features":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a deep conditioning treatment of neural networks")
  Member "adaptive gradient methods with dynamic bound of learning rate":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a metric learning reality check")
  Member "does learning require memorization a short tale about a long tail":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finding online neural update rules by learning to remember")
  Member "robust anomaly detection in images using adversarial autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "simple and effective prevention of mode collapse in deep one class classification")
  Member "efficientnet rethinking model scaling for convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers")
  Member "neural tangent kernel convergence and generalization in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "an empirical study of example forgetting during deep neural network learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning and memorizing representative prototypes for 3d point cloud semantic and instance segmentation")

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)