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: "constrained approximate maximum entropy learning of markov random fields"
  Abstract: Parameter estimation in Markov random fields (MRFs) is a difficult task, in which inference over the network is run in the inner loop of a gradient descent procedure. Replacing exact inference with approximate methods such as loopy belief propagation (LBP) can suffer from poor convergence. In this paper, we provide a different approach for combining MRF learning and Bethe approximation. We consider the dual of maximum likelihood Markov network learning - maximizing entropy with moment matching constraints - and then approximate both the objective and the constraints in the resulting optimization problem. Unlike previous work along these lines (Teh & Welling, 2003), our formulation allows parameter sharing between features in a general log-linear model, parameter regularization and conditional training. We show that piecewise training (Sutton & McCallum, 2005) is a very restricted special case of this formulation. We study two optimization strategies: one based on a single convex approximation and one that uses repeated convex approximations. We show results on several real-world networks that demonstrate that these algorithms can significantly outperform learning with loopy and piecewise. Our results also provide a framework for analyzing the trade-offs of different relaxations of the entropy objective and of the constraints.

Connected through hyperedge HE-1 (induced by source paper "blankets joint posterior score for learning markov network structures"):
  with members:
    - "the libra toolkit for probabilistic models"
    - "a survey on independence based markov networks learning"
    - "the ibmap approach for markov network structure learning"
    - "efficiently inducing features of conditional random fields"

Connected through hyperedge HE-2 (induced by source paper "blankets joint posterior score for learning irregular markov network structures"):
  with members:
    - "the libra toolkit for probabilistic models"
    - "a survey on independence based markov networks learning"
    - "the ibmap approach for markov network structure learning"

Connected through hyperedge HE-3 (induced by source paper "a survey on independence based markov networks learning"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "efficiently inducing features of conditional random fields"

Connected through hyperedge HE-4 (induced by source paper "the ibmap approach for markov network structure learning"):
  with members:
    - "a survey on independence based markov networks learning"
    - "efficiently inducing features of conditional random fields"

Connected through hyperedge HE-5 (induced by source paper "bethe bounds and approximating the global optimum"):
  with members:
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"
    - "accuracy bounds for belief propagation"
    - "what cannot be learned with bethe approximations"
    - "a new class of upper bounds on the log partition function"
    - "the bethe partition function of log supermodular graphical models"
    - "sufficient conditions for convergence of the sum product algorithm"
    - "variational probabilistic inference and the qmr dt network"

Connected through hyperedge HE-6 (induced by source paper "what cannot be learned with bethe approximations"):
  with members:
    - "piecewise training for undirected models"
    - "convexifying the bethe free energy"
    - "a new class of upper bounds on the log partition function"
    - "sufficient conditions for convergence of the sum product algorithm"

Connected through hyperedge HE-7 (induced by source paper "a graphical model formulation of collaborative filtering neighbourhood methods with fast maximum entropy training"):
  with members:
    - "projected subgradient methods for learning sparse gaussians"
    - "properties of bethe free energies and message passing in gaussian models"
    - "gaussian belief propagation solver for systems of linear equations"

Connected through hyperedge HE-8 (induced by source paper "approximated structured prediction for learning large scale graphical models"):
  with members:
    - "discriminative probabilistic models for relational data"
    - "convergent message passing algorithms a unifying view"
    - "a new class of upper bounds on the log partition function"
    - "norm product belief propagation primal dual message passing for approximate inference"
    - "lagrangian relaxation for map estimation in graphical models"
    - "towards low complexity linear programming decoding"

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)