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: "harmless interpolation of noisy data in regression"
  Abstract: A continuing mystery in understanding the empirical success of deep neural networks has been in their ability to achieve zero training error and yet generalize well, even when the training data is noisy and there are more parameters than data points. We investigate this "overparametrization" phenomena in the classical underdetermined linear regression problem, where all solutions that minimize training error interpolate the data, including noise. We give a bound on how well such interpolative solutions can generalize to fresh test data, and show that this bound generically decays to zero with the number of extra features, thus characterizing an explicit benefit of overparameterization. For appropriately sparse linear models, we provide a hybrid interpolating scheme (combining classical sparse recovery schemes with harmless noise-fitting) to achieve generalization error close to the bound on interpolative solutions.

Connected through hyperedge HE-1 (induced by source paper "rethinking parameter counting in deep models effective dimensionality revisited"):
  with members:
    - "fantastic generalization measures and where to find them"
    - "two models of double descent for weak features"
    - "deep double descent where bigger models and more data hurt"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "functional variational bayesian neural networks"
    - "detecting extrapolation with local ensembles"
    - "semi flat minima and saddle points by embedding neural networks to overparameterization"
    - "understanding generalization through visualizations"

Connected through hyperedge HE-2 (induced by source paper "optimal regularization can mitigate double descent"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "two models of double descent for weak features"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "a new look at an old problem a universal learning approach to linear regression"
    - "deep double descent where bigger models and more data hurt"
    - "minimizers of the empirical risk and risk monotonicity"
    - "exact expressions for double descent and implicit regularization via surrogate random design"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"

Connected through hyperedge HE-3 (induced by source paper "overfitting can be harmless for basis pursuit only to a degree"):
  with members:
    - "two models of double descent for weak features"
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "why deep neural networks for function approximation"

Connected through hyperedge HE-4 (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"
    - "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"
    - "dnn or k nn that is the generalize vs memorize question"
    - "privacy preserving prediction"
    - "understanding membership inferences on well generalized learning models"

Connected through hyperedge HE-5 (induced by source paper "exact expressions for double descent and implicit regularization via surrogate random design"):
  with members:
    - "understanding overfitting peaks in generalization error analytical risk curves for l 2 and l 1 penalized interpolation"
    - "traditional and heavy tailed self regularization in neural network models"
    - "two models of double descent for weak features"
    - "distributed estimation of the inverse hessian by determinantal averaging"
    - "implicit regularization in deep matrix factorization"
    - "bayesian experimental design using regularized determinantal point processes"
    - "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression"
    - "implicit regularization in over parameterized neural networks"

Connected through hyperedge HE-6 (induced by source paper "deep double descent where bigger models and more data hurt"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "sgd on neural networks learns functions of increasing complexity"
    - "two models of double descent for weak features"
    - "a new look at an old problem a universal learning approach to linear regression"
    - "attention is all you need"
    - "understanding deep learning requires rethinking generalization"
    - "identity mappings in deep residual 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)