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: "generalized product of experts for automatic and principled fusion of gaussian process predictions"
  Abstract: In this work, we propose a generalized product of experts (gPoE) framework for combining the predictions of multiple probabilistic models. We identify four desirable properties that are important for scalability, expressiveness and robustness, when learning and inferring with a combination of multiple models. Through analysis and experiments, we show that gPoE of Gaussian processes (GP) have these qualities, while no other existing combination schemes satisfy all of them at the same time. The resulting GP-gPoE is highly scalable as individual GP experts can be independently learned in parallel; very expressive as the way experts are combined depends on the input rather than fixed; the combined prediction is still a valid probabilistic model with natural interpretation; and finally robust to unreliable predictions from individual experts.

Connected through hyperedge HE-1 (induced by source paper "product kanerva machines factorized bayesian memory"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "monet unsupervised scene decomposition and representation"
    - "generalization of reinforcement learners with working and episodic memory"
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "metalearned neural memory"
    - "recurrent independent mechanisms"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "shaping belief states with generative environment models for rl"

Connected through hyperedge HE-2 (induced by source paper "wireless traffic prediction with scalable gaussian process framework algorithms and verification"):
  with members:
    - "on asymptotic properties of hyperparameter estimators for kernel based regularization methods"
    - "green heterogeneous cloud radio access networks potential techniques performance trade offs and challenges"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"
    - "recent advances in cloud radio access networks system architectures key techniques and open issues"
    - "electricity demand forecasting by multi task learning"
    - "an approach for spatial temporal traffic modeling in mobile cellular networks"
    - "system architecture and key technologies for 5g heterogeneous cloud radio access networks"

Connected through hyperedge HE-3 (induced by source paper "sequential online prediction in the presence of outliers and change points an instant temporal structure learning approach"):
  with members:
    - "state space model based trust evaluation over wireless sensor networks an iterative particle filter approach"
    - "online fault tolerant dynamic event region detection in sensor networks via trust model"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "robust video object tracking via bayesian model averaging based feature fusion"
    - "evaluating real time anomaly detection algorithms the numenta anomaly benchmark"

Connected through hyperedge HE-4 (induced by source paper "deep structured mixtures of gaussian processes"):
  with members:
    - "bayesian learning of sum product networks"
    - "optimisation of overparametrized sum product networks"
    - "on the latent variable interpretation in sum product networks"
    - "on the relationship between sum product networks and bayesian networks"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-5 (induced by source paper "scalable bayesian non linear matrix completion"):
  with members:
    - "distributed bayesian probabilistic matrix factorization"
    - "distributed bayesian probabilistic matrix factorization"
    - "gaussian process models with parallelization and gpu acceleration"
    - "manifold relevance determination"

Connected through hyperedge HE-6 (induced by source paper "harnessing low fidelity data to accelerate bayesian optimization via posterior regularization"):
  with members:
    - "robust video object tracking via bayesian model averaging based feature fusion"
    - "bayesian inference with posterior regularization and applications to infinite latent svms"

Connected through hyperedge HE-7 (induced by source paper "temporal data fusion at the edge"):
  with members:
    - "sequential online prediction in the presence of outliers and change points an instant temporal structure learning approach"
    - "robust video object tracking via bayesian model averaging based feature fusion"

Connected through hyperedge HE-8 (induced by source paper "multimodal generative models for scalable weakly supervised learning"):
  with members:
    - "unsupervised machine translation using monolingual corpora only"
    - "deep variational canonical correlation analysis"
    - "attention is all you need"
    - "dynamic routing between capsules"
    - "unsupervised neural machine translation"
    - "towards deeper understanding of variational autoencoding models"
    - "generative models of visually grounded imagination"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

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)