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: "dnn or k nn that is the generalize vs memorize question"
  Abstract: This paper studies the relationship between the classification performed by deep neural networks and the $k$-NN decision at the embedding space of these networks. This simple important connection shown here provides a better understanding of the relationship between the ability of neural networks to generalize and their tendency to memorize the training data, which are traditionally considered to be contradicting to each other and here shown to be compatible and complementary. Our results support the conjecture that deep neural networks approach Bayes optimal error rates.

Connected through hyperedge HE-1 (induced by source paper "detecting adversarial samples using influence functions and nearest neighbors"):
  with members:
    - "defense against adversarial images using web scale nearest neighbor search"
    - "defending against adversarial examples with k nearest neighbor"
    - "on evaluating adversarial robustness"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-2 (induced by source paper "does learning require memorization a short tale about a long tail"):
  with members:
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "two models of double descent for weak features"
    - "harmless interpolation of noisy data in regression"
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "towards demystifying membership inference attacks"
    - "privacy preserving prediction"
    - "understanding membership inferences on well generalized learning models"

Connected through hyperedge HE-3 (induced by source paper "understanding geometry of encoder decoder cnns"):
  with members:
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "a differential topological view of challenges in learning with feedforward neural networks"
    - "k space deep learning for accelerated mri"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "optimization landscape and expressivity of deep cnns"

Connected through hyperedge HE-4 (induced by source paper "neural network memorization dissection"):
  with members:
    - "all neural networks are created equal"
    - "understanding deep learning requires rethinking generalization"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "visualizing deep convolutional neural networks using natural pre images"
    - "understanding neural networks through deep visualization"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "the loss surfaces of multilayer networks"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"

Connected through hyperedge HE-5 (induced by source paper "generalization error in deep learning"):
  with members:
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "adversarially robust generalization requires more data"
    - "deep neural networks with multi branch architectures are less non convex"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "exploring generalization in deep learning"
    - "mathematics of deep learning"

Connected through hyperedge HE-6 (induced by source paper "network compression using correlation analysis of layer responses"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "compressing neural networks using the variational information bottleneck"
    - "nisp pruning networks using neuron importance score propagation"
    - "domain adaptive deep network compression"
    - "thinet a filter level pruning method for deep neural network compression"
    - "compression aware training of deep networks"

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)