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 "bethe bounds and approximating the global optimum"):
  Abstract: Inference in general Markov random fields (MRFs) is NP-hard, though identifying the maximum a posteriori (MAP) configuration of pairwise MRFs with submodular cost functions is efficiently solvable using graph cuts. Marginal inference, however, even for this restricted class, is in #P. We prove new formulations of derivatives of the Bethe free energy, provide bounds on the derivatives and bracket the locations of stationary points, introducing a new technique called Bethe bound propagation. Several results apply to pairwise models whether associative or not. Applying these to discretized pseudo-marginals in the associative case we present a polynomial time approximation scheme for global optimization provided the maximum degree is $O(\log n)$, and discuss several extensions.

Includes members:
  Member "belief optimization for binary networks a stable alternative to loopy belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lifted hybrid variational inference")
  Member "accuracy bounds for belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "anytime exact belief propagation")
  Member "constrained approximate maximum entropy learning of markov random fields":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "blankets joint posterior score for learning markov network structures")
  Member "what cannot be learned with bethe approximations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating the bethe partition function")
  Member "a new class of upper bounds on the log partition function":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "visual semantic information pursuit a survey")
  Member "the bethe partition function of log supermodular graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lifted hybrid variational inference")
  Member "sufficient conditions for convergence of the sum product algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "factorized graph representations for semi supervised learning from sparse data")
  Member "variational probabilistic inference and the qmr dt network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the accuracy vs coverage trade off in patient facing diagnosis models")

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)