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: "learning scalable deep kernels with recurrent structure"
  Abstract: Many applications in speech, robotics, finance, and biology deal with sequential data, where ordering matters and recurrent structures are common. However, this structure cannot be easily captured by standard kernel functions. To model such structure, we propose expressive closed-form kernel functions for Gaussian processes. The resulting model, GP-LSTM, fully encapsulates the inductive biases of long short-term memory (LSTM) recurrent networks, while retaining the non-parametric probabilistic advantages of Gaussian processes. We learn the properties of the proposed kernels by optimizing the Gaussian process marginal likelihood using a new provably convergent semi-stochastic gradient procedure and exploit the structure of these kernels for scalable training and prediction. This approach provides a practical representation for Bayesian LSTMs. We demonstrate state-of-the-art performance on several benchmarks, and thoroughly investigate a consequential autonomous driving application, where the predictive uncertainties provided by GP-LSTM are uniquely valuable.

Connected through hyperedge HE-1 (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"
    - "deep kernel learning"
    - "adam a method for stochastic optimization"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "learnable embedding space for efficient neural architecture compression"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "regularized evolution for image classifier architecture search"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "neural architecture optimization"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "darts differentiable architecture search"

Connected through hyperedge HE-3 (induced by source paper "on exact computation with an infinitely wide neural net"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "width provably matters in optimization for deep linear neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "deep convolutional gaussian processes"

Connected through hyperedge HE-4 (induced by source paper "recurrent attentive neural process for sequential data"):
  with members:
    - "sequential neural processes"
    - "attentive neural processes"
    - "the functional neural process"
    - "neural processes"
    - "consistent generative query networks"
    - "conditional neural processes"
    - "structural recurrent neural network srnn for group activity analysis"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "multiplex communities and the emergence of international conflict"):
  with members:
    - "identification interpretability and bayesian word embeddings"
    - "hiding individuals and communities in a social network"
    - "understanding state preferences with text as data introducing the un general debate corpus"
    - "the ground truth about metadata and community detection in networks"
    - "higher order organization of complex networks"
    - "community extraction in multilayer networks with heterogeneous community structure"
    - "a new framework for dynamical models on multiplex networks"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "on the expected behaviour of noise regularised deep neural networks as gaussian processes"):
  with members:
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "variational auto encoded deep gaussian processes"
    - "steps toward deep kernel methods from infinite neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-7 (induced by source paper "bayesian layers a module for neural network uncertainty"):
  with members:
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "image transformer"
    - "learning latent dynamics for planning from pixels"
    - "uncertainty estimations by softplus normalization in bayesian convolutional neural networks with variational inference"
    - "pyro deep universal probabilistic programming"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "mesh tensorflow deep learning for supercomputers"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-8 (induced by source paper "semi supervised deep kernel learning regression with unlabeled data by minimizing predictive variance"):
  with members:
    - "adversarial constraint learning for structured prediction"
    - "infrastructure quality assessment in africa using satellite imagery and deep learning"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"
    - "deep kernel learning"
    - "semi supervised learning with ladder networks"
    - "transfer learning from deep features for remote sensing and poverty mapping"

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)