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: "supervised feature selection via dependence estimation"
  Abstract: We introduce a framework for filtering features that employs the Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence between the features and the labels. The key idea is that good features should maximise such dependence. Feature selection for various supervised learning problems (including classification and regression) is unified under this framework, and the solutions can be approximated using a backward-elimination algorithm. We demonstrate the usefulness of our method on both artificial and real world datasets.

Connected through hyperedge HE-1 (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-2 (induced by source paper "feature selection using stochastic gates"):
  with members:
    - "optuna a next generation hyperparameter optimization framework"
    - "implicit reparameterization gradients"
    - "large scale nonlinear variable selection via kernel random features"
    - "dropout feature ranking for deep learning models"
    - "xgboost a scalable tree boosting system"
    - "why should i trust you explaining the predictions of any classifier"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "variational inference for monte carlo objectives"

Connected through hyperedge HE-3 (induced by source paper "deep kernel learning for clustering"):
  with members:
    - "deep subspace clustering networks"
    - "mmd gan towards deeper understanding of moment matching network"
    - "deep kernel learning"
    - "unsupervised deep embedding for clustering analysis"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "graph autoencoder based unsupervised feature selection with broad and local data structure preservation"):
  with members:
    - "feature selection a data perspective"
    - "deep learning in neural networks"
    - "unsupervised feature selection with adaptive structure learning"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "large scale nonlinear variable selection via kernel random features"):
  with members:
    - "group sparse additive models"
    - "high dimensional non linear variable selection through hierarchical kernel learning"
    - "consistency of the group lasso and multiple kernel learning"

Connected through hyperedge HE-6 (induced by source paper "online feature selection with group structure analysis"):
  with members:
    - "gradient boosted feature selection"
    - "two layer feature reduction for sparse group lasso via decomposition of convex sets"
    - "unified structured learning for simultaneous human pose estimation and garment attribute classification"

Connected through hyperedge HE-7 (induced by source paper "selecting a small set of optimal gestures from an extensive lexicon"):
  with members:
    - "generalized fisher score for feature selection"

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)