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: "l2 regularization for learning kernels"
  Abstract: The choice of the kernel is critical to the success of many learning algorithms but it is typically left to the user. Instead, the training data can be used to learn the kernel by selecting it out of a given family, such as that of non-negative linear combinations of p base kernels, constrained by a trace or L1 regularization. This paper studies the problem of learning kernels with the same family of kernels but with an L2 regularization instead, and for regression problems. We analyze the problem of learning kernels with ridge regression. We derive the form of the solution of the optimization problem and give an efficient iterative algorithm for computing that solution. We present a novel theoretical analysis of the problem based on stability and give learning bounds for orthogonal kernels that contain only an additive term O(pp/m) when compared to the standard kernel ridge regression stability bound. We also report the results of experiments indicating that L1 regularization can lead to modest improvements for a small number of kernels, but to performance degradations in larger-scale cases. In contrast, L2 regularization never degrades performance and in fact achieves significant improvements with a large number of kernels.

Connected through hyperedge HE-1 (induced by source paper "dfnets spectral cnns for graphs with feedback looped filters"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "graph neural networks with convolutional arma filters"
    - "autoregressive moving average graph filtering"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "densely connected convolutional networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "revisiting semi supervised learning with graph embeddings"

Connected through hyperedge HE-2 (induced by source paper "ai and holistic review informing human reading in college admissions"):
  with members:
    - "toward controlling discrimination in online ad auctions"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "datasheets for datasets"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "distributed representations of words and phrases and their compositionality"
    - "nltk the natural language toolkit"

Connected through hyperedge HE-3 (induced by source paper "skeptical deep learning with distribution correction"):
  with members:
    - "deep learning is robust to massive label noise"
    - "early stopping without a validation set"
    - "toward robustness against label noise in training deep discriminative neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a downsampled variant of imagenet as an alternative to the cifar datasets"
    - "temporal ensembling for semi supervised learning"
    - "wide residual networks"
    - "learning with symmetric label noise the importance of being unhinged"

Connected through hyperedge HE-4 (induced by source paper "multiple kernel learning and automatic subspace relevance determination for high dimensional neuroimaging data"):
  with members:
    - "sparse 3d convolutional neural networks"
    - "deepstereo learning to predict new views from the world s imagery"
    - "predicting alzheimer s disease a neuroimaging study with 3d convolutional neural networks"
    - "going deeper with convolutions"
    - "learning to generate chairs tables and cars with convolutional networks"
    - "variable noise and dimensionality reduction for sparse gaussian processes"
    - "building high level features using large scale unsupervised learning"
    - "robustness and regularization of support vector machines"

Connected through hyperedge HE-5 (induced by source paper "a metric learning based framework for support vector machines and multiple kernel learning"):
  with members:
    - "a metric learning perspective of svm on the relation of svm and lmnn"

Connected through hyperedge HE-6 (induced by source paper "multiple kernel learning from noisy labels by stochastic programming"):
  with members:
    - "robust metric learning by smooth optimization"
    - "consistency of the group lasso and multiple kernel learning"

Connected through hyperedge HE-7 (induced by source paper "insights from classifying visual concepts with multiple kernel learning"):
  with members:
    - "non sparse regularization for multiple kernel learning"

Connected through hyperedge HE-8 (induced by source paper "learning discriminative metrics via generative models and kernel learning"):
  with members:
    - "positive semidefinite metric learning with boosting"

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)