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: "fast variational inference in the conjugate exponential family"
  Abstract: We present a general method for deriving collapsed variational inference algo- rithms for probabilistic models in the conjugate exponential family. Our method unifies many existing approaches to collapsed variational inference. Our collapsed variational inference leads to a new lower bound on the marginal likelihood. We exploit the information geometry of the bound to derive much faster optimization methods based on conjugate gradients for these models. Our approach is very general and is easily applied to any model where the mean field update equations have been derived. Empirically we show significant speed-ups for probabilistic models optimized using our bound.

Connected through hyperedge HE-1 (induced by source paper "a probabilistic representation of deep learning"):
  with members:
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "exploring generalization in deep learning"
    - "emergence of invariance and disentanglement in deep representations"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "opening the black box of deep neural networks via information"
    - "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "conditional random fields as recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "a view of estimation of distribution algorithms through the lens of expectation maximization"):
  with members:
    - "conditioning by adaptive sampling for robust design"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "new insights and perspectives on the natural gradient method"
    - "path integral policy improvement with covariance matrix adaptation"

Connected through hyperedge HE-3 (induced by source paper "advances in variational inference"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "iterative amortized inference"
    - "inference suboptimality in variational autoencoders"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "faster coordinate descent via adaptive importance sampling"
    - "the numerics of gans"
    - "stein variational policy gradient"

Connected through hyperedge HE-4 (induced by source paper "breaking sticks and ambiguities with adaptive skip gram"):
  with members:
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "one billion word benchmark for measuring progress in statistical language modeling"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "probabilistic line searches for stochastic optimization"):
  with members:
    - "learning to optimize"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "collapsed variational bayes inference of infinite relational model"):
  with members:
    - "stochastic collapsed variational bayesian inference for latent dirichlet allocation"
    - "community detection in networks with node attributes"
    - "on smoothing and inference for topic models"
    - "rethinking collapsed variational bayes inference for lda"
    - "an infinite latent attribute model for network data"

Connected through hyperedge HE-7 (induced by source paper "gaussian processes for big data"):
  with members:
    - "manifold relevance determination"

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)