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: "bayesian learning in undirected graphical models approximate mcmc algorithms"
  Abstract: Bayesian learning in undirected graphical models|computing posterior distributions over parameters and predictive quantities is exceptionally difficult. We conjecture that for general undirected models, there are no tractable MCMC (Markov Chain Monte Carlo) schemes giving the correct equilibrium distribution over parameters. While this intractability, due to the partition function, is familiar to those performing parameter optimisation, Bayesian learning of posterior distributions over undirected model parameters has been unexplored and poses novel challenges. we propose several approximate MCMC schemes and test on fully observed binary models (Boltzmann machines) for a small coronary heart disease data set and larger artificial systems. While approximations must perform well on the model, their interaction with the sampling scheme is also important. Samplers based on variational mean- field approximations generally performed poorly, more advanced methods using loopy propagation, brief sampling and stochastic dynamics lead to acceptable parameter posteriors. Finally, we demonstrate these techniques on a Markov random field with hidden variables.

Connected through hyperedge HE-1 (induced by source paper "a theoretical connection between statistical physics and reinforcement learning"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "efficient exploration and value function generalization in deterministic systems"

Connected through hyperedge HE-2 (induced by source paper "taming the curse of dimensionality discrete integration by hashing and optimization"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "a scalable and nearly uniform generator of sat witnesses"
    - "improving the accuracy and efficiency of map inference for markov logic"
    - "map complexity results and approximation methods"
    - "on the partition function and random maximum a posteriori perturbations"
    - "tightening lp relaxations for map using message passing"
    - "a decidable dichotomy theorem on directed graph homomorphisms with non negative weights"

Connected through hyperedge HE-3 (induced by source paper "bayesian inference with posterior regularization and applications to infinite latent svms"):
  with members:
    - "a convex formulation for learning task relationships in multi task learning"
    - "alternating projections for learning with expectation constraints"
    - "bayesian random fields the bethe laplace approximation"
    - "on bregman distances and divergences of probability measures"

Connected through hyperedge HE-4 (induced by source paper "convergence rates of biased stochastic optimization for learning sparse ising models"):
  with members:
    - "hybrid deterministic stochastic methods for data fitting"
    - "complexity of inference in graphical models"
    - "on learning discrete graphical models using greedy methods"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

Connected through hyperedge HE-5 (induced by source paper "herding dynamic weights for partially observed random field models"):
  with members:
    - "bayesian random fields the bethe laplace approximation"

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)