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: "a new look at an old problem a universal learning approach to linear regression"
  Abstract: Linear regression is a classical paradigm in statistics. A new look at it is provided via the lens of universal learning. In applying universal learning to linear regression the hypotheses class represents the label $y\in {\cal R}$ as a linear combination of the feature vector $x^T\theta$ where $x\in {\cal R}^M$, within a Gaussian error. The Predictive Normalized Maximum Likelihood (pNML) solution for universal learning of individual data can be expressed analytically in this case, as well as its associated learnability measure. Interestingly, the situation where the number of parameters $M$ may even be larger than the number of training samples $N$ can be examined. As expected, in this case learnability cannot be attained in every situation; nevertheless, if the test vector resides mostly in a subspace spanned by the eigenvectors associated with the large eigenvalues of the empirical correlation matrix of the training data, linear regression can generalize despite the fact that it uses an ``over-parametrized'' model. We demonstrate the results with a simulation of fitting a polynomial to data with a possibly large polynomial degree.

Connected through hyperedge HE-1 (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"
    - "harmless interpolation of noisy data in 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-2 (induced by source paper "harmless interpolation of noisy data in regression"):
  with members:
    - "understanding overfitting peaks in generalization error analytical risk curves for l 2 and l 1 penalized interpolation"
    - "two models of double descent for weak features"
    - "kernel and deep regimes in overparametrized models"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "a convergence theory for deep learning via over parameterization"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "exploring generalization in deep learning"

Connected through hyperedge HE-3 (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"
    - "harmless interpolation of noisy data in regression"
    - "attention is all you need"
    - "understanding deep learning requires rethinking generalization"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-4 (induced by source paper "deep pnml predictive normalized maximum likelihood for deep neural networks"):
  with members:
    - "universal supervised learning for individual data"
    - "confidence prediction for lexicon free ocr"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "deep residual learning for image recognition"

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)