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 "an ilp solver for multi label mrfs with connectivity constraints"):
  Abstract: Integer Linear Programming (ILP) formulations of Markov random fields (MRFs) models with global connectivity priors were investigated previously in computer vision, e.g., \cite{globalinter,globalconn}. In these works, only Linear Programming (LP) relaxations \cite{globalinter,globalconn} or simplified versions \cite{graphcutbase} of the problem were solved. This paper investigates the ILP of multi-label MRF with exact connectivity priors via a branch-and-cut method, which provably finds globally optimal solutions. The method enforces connectivity priors iteratively by a cutting plane method, and provides feasible solutions with a guarantee on sub-optimality even if we terminate it earlier. The proposed ILP can also be applied as a post-processing method on top of any existing multi-label segmentation approach. We demonstrate the power and usefulness of our model by several experiments on the BSDS500 image dataset, as well as on medical images with trained probability maps.

Includes members:
  Member "superpixels an evaluation of the state of the art":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi scale superpatch matching using dual superpixel descriptors")
  Member "unbiased shape compactness for segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discretely constrained deep network for weakly supervised segmentation")
  Member "3d fully convolutional networks for subcortical segmentation in mri a large scale study":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semi supervised few shot learning for medical image segmentation")
  Member "the minimum cost connected subgraph problem in medical image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient algorithms for moral lineage tracing")
  Member "scribblesup scribble supervised convolutional networks for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to adapt class specific features across domains for semantic segmentation")
  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")

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)