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: "efficient learning of structured predictors in general graphical models"
  Abstract: In this paper we derive an efficient message-passing algorithm to learn the parameters of structured predictors in general graphical models. We define the extended log-loss, which relates the log-loss of CRFs and the hinge-loss of structured SVMs through a temperature parameter. We then investigate the primal and dual properties of the extended log-loss, showing that the dual programs of both CRFs and structured SVMs perform moment matching using different selection rules. Utilizing the graphical models of the predictors, we describe a low dimensional extended log-loss formulation, which is derived from pseudo moment matching and a fractional entropy approximation selection rule in the dual setting. We propose an efficient message-passing algorithm that is guaranteed to converge to the optimum of the low dimensional primal and dual programs. Unlike many of the existing approaches, this allows us to learn efficiently high-order graphical models, over regions of any size, and very large number of parameters. We demonstrate the effectiveness of our approach, while presenting state-of-the-art results in stereo estimation, semantic segmentation, shape reconstruction, and indoor scene understanding.

Connected through hyperedge HE-1 (induced by source paper "structured learning via logistic regression"):
  with members:
    - "learning graphical model parameters with approximate marginal inference"

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)