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: "algorithms for learning kernels based on centered alignment"
  Abstract: This paper presents new and effective algorithms for learning kernels. In particular, as shown by our empirical results, these algorithms consistently outperform the so-called uniform combination solution that has proven to be difficult to improve upon in the past, as well as other algorithms for learning kernels based on convex combinations of base kernels in both classification and regression. Our algorithms are based on the notion of centered alignment which is used as a similarity measure between kernels or kernel matrices. We present a number of novel algorithmic, theoretical, and empirical results for learning kernels based on our notion of centered alignment. In particular, we describe efficient algorithms for learning a maximum alignment kernel by showing that the problem can be reduced to a simple QP and discuss a one-stage algorithm for learning both a kernel and a hypothesis based on that kernel using an alignment-based regularization. Our theoretical results include a novel concentration bound for centered alignment between kernel matrices, the proof of the existence of effective predictors for kernels with high alignment, both for classification and for regression, and the proof of stability-based generalization bounds for a broad family of algorithms for learning kernels based on centered alignment. We also report the results of experiments with our centered alignment-based algorithms in both classification and regression.

Connected through hyperedge HE-1 (induced by source paper "entangled watermarks as a defense against model extraction"):
  with members:
    - "similarity of neural network representations revisited"
    - "high fidelity extraction of neural network models"
    - "a framework for the extraction of deep neural networks by leveraging public data"
    - "energy and policy considerations for deep learning in nlp"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "knockoff nets stealing functionality of black box models"

Connected through hyperedge HE-2 (induced by source paper "correlations between word vector sets"):
  with members:
    - "correlation coefficients and semantic textual similarity"
    - "similarity of neural network representations revisited"
    - "model comparison for semantic grouping"
    - "don t settle for average go for the max fuzzy sets and max pooled word vectors"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "a rank based similarity metric for word embeddings"

Connected through hyperedge HE-3 (induced by source paper "similarity of neural network representations revisited"):
  with members:
    - "tensor2tensor for neural machine translation"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "towards understanding learning representations to what extent do different neural networks learn the same representation"
    - "skip connections eliminate singularities"
    - "attention is all you need"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "perceptual losses for real time style transfer and super resolution"
    - "density estimation using real nvp"

Connected through hyperedge HE-4 (induced by source paper "learning from brains how to regularize machines"):
  with members:
    - "similarity of neural network representations revisited"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "a neurobiological evaluation metric for neural network model search"
    - "towards the first adversarially robust neural network model on mnist"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "generalisation in humans and deep neural networks"
    - "sparsefool a few pixels make a big difference"

Connected through hyperedge HE-5 (induced by source paper "an empirical study on post processing methods for word embeddings"):
  with members:
    - "similarity of neural network representations revisited"
    - "hyperbolic attention networks"
    - "embedding text in hyperbolic spaces"
    - "on the dimensionality of word embedding"
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "all but the top simple and effective postprocessing for word representations"
    - "poincar e embeddings for learning hierarchical representations"
    - "word translation without parallel data"

Connected through hyperedge HE-6 (induced by source paper "multiple kernel learning from u statistics of empirical measures in the feature space"):
  with members:
    - "learning bounds for greedy approximation with explicit feature maps from multiple kernels"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"
    - "transfer learning from deep features for remote sensing and poverty mapping"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "random fourier features via fast surrogate leverage weighted sampling"):
  with members:
    - "nystrom landmark sampling and regularized christoffel functions"
    - "but how does it work in theory linear svm with random features"
    - "low precision random fourier features for memory constrained kernel approximation"
    - "quadrature based features for kernel approximation"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "gaussian quadrature for kernel features"
    - "orthogonal random features"
    - "input sparsity time low rank approximation via ridge leverage score sampling"

Connected through hyperedge HE-8 (induced by source paper "spectral analysis of kernel and neural embeddings optimization and generalization"):
  with members:
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "invariance of weight distributions in rectified mlps"
    - "understanding deep learning requires rethinking generalization"
    - "deep learning in neural networks"
    - "representation learning a review and new perspectives"
    - "a tutorial on spectral clustering"

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)