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 "map inference via block coordinate frank wolfe algorithm"):
  Abstract: We present a new proximal bundle method for Maximum-A-Posteriori (MAP) inference in structured energy minimization problems. The method optimizes a Lagrangean relaxation of the original energy minimization problem using a multi plane block-coordinate Frank-Wolfe method that takes advantage of the specific structure of the Lagrangean decomposition. We show empirically that our method outperforms state-of-the-art Lagrangean decomposition based algorithms on some challenging Markov Random Field, multi-label discrete tomography and graph matching problems.

Includes members:
  Member "efficient linear programming for dense crfs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "proximal mean field for neural network quantization")
  Member "minding the gaps for block frank wolfe optimization of structured svms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "curvature of feasible sets in offline and online optimization")
  Member "a dual ascent framework for lagrangean decomposition of combinatorial problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "synchronisation of partial multi matchings via non negative factorisations")
  Member "a new look at reweighted message passing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "continuous relaxation of map inference a nonconvex perspective")
  Member "submodular relaxation for inference in markov random fields":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "powerset convolutional neural networks")
  Member "a comparative study of modern inference techniques for structured discrete energy minimization problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "worst case polynomial time exact map inference on discrete models with global dependencies")
  Member "a multi plane block coordinate frank wolfe algorithm for training structural svms with a costly max oracle":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "guaranteed parameter estimation for discrete energy minimization")
  Member "block coordinate frank wolfe optimization for structural svms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "safe screening for the generalized conditional gradient method")

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)