human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "approximated structured prediction for learning large scale graphical models"):
  Abstract: In this paper we propose an approximated structured prediction framework for large scale graphical models and derive message-passing algorithms for learning their parameters eciently. We rst relate CRFs and structured SVMs and show that in CRFs a variant of the log-partition function, known as soft-max, smoothly approximates the hinge loss function of structured SVMs. We then propose an intuitive approximation for the structured prediction problem, using duality, based on local entropy approximations and derive an ecient message-passing algorithm that is guaranteed to converge to the optimum for concave entropy approximations. Unlike existing approaches, this allows us to learn eciently graphical models with cycles and very large number of parameters. We demonstrate the eectiveness of our approach in an image denoising task. This task was previously solved by sharing parameters across cliques. In contrast, our algorithm is able to eciently learn large number of parameters resulting in orders of magnitude better prediction.

Includes members:
  Member "discriminative probabilistic models for relational data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning distributional programs for relational autocompletion")
  Member "constrained approximate maximum entropy learning of markov random fields":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "blankets joint posterior score for learning markov network structures")
  Member "convergent message passing algorithms a unifying view":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graph structured prediction energy networks")
  Member "a new class of upper bounds on the log partition function":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "visual semantic information pursuit a survey")
  Member "norm product belief propagation primal dual message passing for approximate inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scalable explanation of inferences on large graphs")
  Member "lagrangian relaxation for map estimation in graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "map inference via l2 sphere linear program reformulation")
  Member "towards low complexity linear programming decoding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adaptive linear programming decoding of nonbinary linear codes over prime fields")

Question: Please tell me which class the source paper 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)