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: "exploring large feature spaces with hierarchical multiple kernel learning"
  Abstract: For supervised and unsupervised learning, positive definite kernels allow to use large and potentially infinite dimensional feature spaces with a computational cost that only depends on the number of observations. This is usually done through the penalization of predictor functions by Euclidean or Hilbertian norms. In this paper, we explore penalizing by sparsity-inducing norms such as the l1-norm or the block l1-norm. We assume that the kernel decomposes into a large sum of individual basis kernels which can be embedded in a directed acyclic graph; we show that it is then possible to perform kernel selection through a hierarchical multiple kernel learning framework, in polynomial time in the number of selected kernels. This framework is naturally applied to non linear variable selection; our extensive simulations on synthetic datasets and datasets from the UCI repository show that efficiently exploring the large feature space through sparsity-inducing norms leads to state-of-the-art predictive performance.

Connected through hyperedge HE-1 (induced by source paper "on the expressive power of kernel methods and the efficiency of kernel learning by association schemes"):
  with members:
    - "classification with low rank and missing data"
    - "algorithms for learning kernels based on centered alignment"

Connected through hyperedge HE-2 (induced by source paper "robustness guarantees for bayesian inference with gaussian processes"):
  with members:
    - "central limit model checking"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "certified defenses against adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "how wrong am i studying adversarial examples and their impact on uncertainty in gaussian process machine learning models"
    - "dropout inference in bayesian neural networks with alpha divergences"
    - "feature guided black box safety testing of deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "differentiable compositional kernel learning for gaussian processes"):
  with members:
    - "deep kernel learning"
    - "sum product networks a new deep architecture"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "fast convnets using group wise brain damage"):
  with members:
    - "tensorizing neural networks"
    - "perforatedcnns acceleration through elimination of redundant convolutions"
    - "compressing neural networks with the hashing trick"
    - "data dependent path normalization in neural networks"
    - "deep learning with limited numerical precision"
    - "exploiting linear structure within convolutional networks for efficient evaluation"
    - "fitnets hints for thin deep nets"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "crh a simple benchmark approach to continuous hashing"):
  with members:
    - "hashing with binary autoencoders"

Connected through hyperedge HE-6 (induced by source paper "voted kernel regularization"):
  with members:
    - "ensembles of kernel predictors"

Connected through hyperedge HE-7 (induced by source paper "breaking the curse of dimensionality with convex neural networks"):
  with members:
    - "on the equivalence between quadrature rules and random features"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "on the computational efficiency of training neural networks"
    - "computational aspects of the hausdorff distance in unbounded dimension"
    - "convex relaxations of structured matrix factorizations"
    - "duality between subgradient and conditional gradient methods"
    - "learning from an optimization viewpoint"
    - "consistency of the group lasso and multiple kernel learning"

Connected through hyperedge HE-8 (induced by source paper "a convex feature learning formulation for latent task structure discovery"):
  with members:
    - "clustered multi task learning a convex formulation"

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)