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 "efficient semidefinite branch and cut for map mrf inference"):
  Abstract: We propose a Branch-and-Cut (B&C) method for solving general MAP-MRF inference problems. The core of our method is a very efficient bounding procedure, which combines scalable semidefinite programming (SDP) and a cutting-plane method for seeking violated constraints. In order to further speed up the computation, several strategies have been exploited, including model reduction, warm start and removal of inactive constraints. #R##N#We analyze the performance of the proposed method under different settings, and demonstrate that our method either outperforms or performs on par with state-of-the-art approaches. Especially when the connectivities are dense or when the relative magnitudes of the unary costs are low, we achieve the best reported results. Experiments show that the proposed algorithm achieves better approximation than the state-of-the-art methods within a variety of time budgets on challenging non-submodular MAP-MRF inference problems.

Includes members:
  Member "efficient sdp inference for fully connected crfs based on low rank decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "accelerated inference in markov random fields via smooth riemannian optimization")
  Member "crf learning with cnn features for image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised visual feature learning with spike timing dependent plasticity how far are we from traditional feature learning approaches")
  Member "maximum persistency via iterative relaxed inference with graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a discriminative view of mrf pre processing algorithms")
  Member "a comparative study of modern inference techniques for structured discrete energy minimization problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "worst case polynomial time exact map inference on discrete models with global dependencies")
  Member "maximum persistency in energy minimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a discriminative view of mrf pre processing algorithms")
  Member "partial optimality by pruning for map inference with general graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a discriminative view of mrf pre processing algorithms")
  Member "scalable semidefinite relaxation for maximum a posterior estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "path invariant map networks")
  Member "a fast semidefinite approach to solving binary quadratic problems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "convex optimisation for inverse kinematics")

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.CV(Computer Vision and Pattern Recognition)