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: "deep kernel learning"
  Abstract: We introduce scalable deep kernels, which combine the structural properties of deep learning architectures with the non-parametric flexibility of kernel methods. Specifically, we transform the inputs of a spectral mixture base kernel with a deep architecture, using local kernel interpolation, inducing points, and structure exploiting (Kronecker and Toeplitz) algebra for a scalable kernel representation. These closed-form kernels can be used as drop-in replacements for standard kernels, with benefits in expressive power and scalability. We jointly learn the properties of these kernels through the marginal likelihood of a Gaussian process. Inference and learning cost $O(n)$ for $n$ training points, and predictions cost $O(1)$ per test point. On a large and diverse collection of applications, including a dataset with 2 million examples, we show improved performance over scalable Gaussian processes with flexible kernel learning models, and stand-alone deep architectures.

Connected through hyperedge HE-1 (induced by source paper "effective diversity in population based reinforcement learning"):
  with members:
    - "novelty search for deep reinforcement learning policy network weights by action sequence edit metric distance"
    - "collaborative evolutionary reinforcement learning"
    - "provably robust blackbox optimization for reinforcement learning"
    - "behavior guided reinforcement learning"
    - "evolutionary reinforcement learning for sample efficient multiagent coordination"
    - "adapting behaviour for learning progress"
    - "attraction repulsion actor critic for continuous control reinforcement learning"
    - "a generalized framework for population based training"

Connected through hyperedge HE-2 (induced by source paper "being bayesian about categorical probability"):
  with members:
    - "when does label smoothing help"
    - "momentum contrast for unsupervised visual representation learning"
    - "functional variational bayesian neural networks"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "evidential deep learning to quantify classification uncertainty"
    - "a variational dirichlet framework for out of distribution detection"
    - "realistic evaluation of deep semi supervised learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "the case for bayesian deep learning"):
  with members:
    - "output constrained bayesian neural networks"
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "functional variational bayesian neural networks"
    - "evaluating scalable bayesian deep learning methods for robust computer vision"
    - "understanding generalization through visualizations"
    - "subspace inference for bayesian deep learning"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "the functional neural process"

Connected through hyperedge HE-4 (induced by source paper "fedloc federated learning framework for cooperative localization and location data processing"):
  with members:
    - "human motion trajectory prediction a survey"
    - "towards federated learning at scale system design"
    - "contextualized spatial temporal network for taxi origin destination demand prediction"
    - "on exact computation with an infinitely wide neural net"
    - "wireless traffic prediction with scalable gaussian process framework algorithms and verification"
    - "federated machine learning concept and applications"
    - "federated learning for ultra reliable low latency v2v communications"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-5 (induced by source paper "compressive mri quantification using convex spatiotemporal priors and deep auto encoders"):
  with members:
    - "deep mr fingerprinting with total variation and low rank subspace priors"
    - "magnetic resonance fingerprinting reconstruction via spatiotemporal convolutional neural networks"
    - "magnetic resonance fingerprinting using recurrent neural networks"
    - "coverblip accelerated and scalable iterative matched filtering for magnetic resonance fingerprint reconstruction"
    - "geometry of deep learning for magnetic resonance fingerprinting"
    - "better than real complex valued neural nets for mri fingerprinting"
    - "mr fingerprinting deep reconstruction network drone"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "multi source deep gaussian process kernel learning"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "interpretable deep gaussian processes with moments"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "deep multi fidelity gaussian processes"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "generalized denoising auto encoders as generative models"
    - "gaussian processes for big data"
    - "what regularized auto encoders learn from the data generating distribution"

Connected through hyperedge HE-7 (induced by source paper "stepwise model selection for sequence prediction via deep kernel learning"):
  with members:
    - "relaxed weight sharing effectively modeling time varying relationships in clinical time series"
    - "deep sets"
    - "learning scalable deep kernels with recurrent structure"
    - "adam a method for stochastic optimization"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"):
  with members:
    - "a mean field theory of batch normalization"
    - "entropy and mutual information in models of deep neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "exploration by random network distillation"
    - "fisher information and natural gradient learning of random deep networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "large scale study of curiosity driven learning"

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)