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: "differentiable compositional kernel learning for gaussian processes"
  Abstract: The generalization properties of Gaussian processes depend heavily on the choice of kernel, and this choice remains a dark art. We present the Neural Kernel Network (NKN), a flexible family of kernels represented by a neural network. The NKN architecture is based on the composition rules for kernels, so that each unit of the network corresponds to a valid kernel. It can compactly approximate compositional kernel structures such as those used by the Automatic Statistician (Lloyd et al., 2014), but because the architecture is differentiable, it is end-to-end trainable with gradient-based optimization. We show that the NKN is universal for the class of stationary kernels. Empirically we demonstrate pattern discovery and extrapolation abilities of NKN on several tasks that depend crucially on identifying the underlying structure, including time series and texture extrapolation, as well as Bayesian optimization.

Connected through hyperedge HE-1 (induced by source paper "convolutional spectral kernel learning"):
  with members:
    - "automated spectral kernel learning"
    - "orthogonal deep neural networks"
    - "functional variational bayesian neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "pytorch an imperative style high performance deep learning library"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "universality of deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "functional variational bayesian neural networks"):
  with members:
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "eigenvalue corrected noisy natural gradient"
    - "scalable gaussian processes with billions of inducing inputs via tensor train decomposition"
    - "noisy natural gradient as variational inference"
    - "amortised map inference for image super resolution"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"
    - "fastfood approximate kernel expansions in loglinear time"
    - "an information theoretic analysis of thompson sampling"

Connected through hyperedge HE-3 (induced by source paper "interpretable deep gaussian processes with moments"):
  with members:
    - "understanding deep learning requires rethinking generalization"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "deep kernel learning"
    - "convolutional kernel networks"
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-4 (induced by source paper "neural processes"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "probabilistic model agnostic meta learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to reinforcement learn"
    - "one shot learning with memory augmented neural networks"
    - "generating sentences from a continuous space"

Connected through hyperedge HE-5 (induced by source paper "deep convolutional gaussian processes"):
  with members:
    - "densely connected convolutional networks"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"

Connected through hyperedge HE-6 (induced by source paper "deep learning with differential gaussian process flows"):
  with members:
    - "tensorflow a system for large scale machine learning"
    - "gaussian processes for big data"

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)