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 "a discriminative view of mrf pre processing algorithms"):
  Abstract: While Markov Random Fields (MRFs) are widely used in computer vision, they present a quite challenging inference problem. MRF inference can be accelerated by pre-processing techniques like Dead End Elimination (DEE) or QPBO-based approaches which compute the optimal labeling of a subset of variables. These techniques are guaranteed to never wrongly label a variable but they often leave a large number of variables unlabeled. We address this shortcoming by interpreting pre-processing as a classification problem, which allows us to trade off false positives (i.e., giving a variable an incorrect label) versus false negatives (i.e., failing to label a variable). We describe an efficient discriminative rule that finds optimal solutions for a subset of variables. Our technique provides both per-instance and worst-case guarantees concerning the quality of the solution. Empirical studies were conducted over several benchmark datasets. We obtain a speedup factor of 2 to 12 over expansion moves without preprocessing, and on difficult non-submodular energy functions produce slightly lower energy.

Includes members:
  Member "full flow optical flow estimation by global optimization over regular grids":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sceneflowfields multi frame matching visibility prediction and robust interpolation for scene flow estimation")
  Member "maximum persistency via iterative relaxed inference with graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "complexity of discrete energy minimization problems")
  Member "maximum persistency in energy minimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "global hypothesis generation for 6d object pose estimation")
  Member "partial optimality by pruning for map inference with general graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "image labeling by assignment")
  Member "tightening lp relaxations for map using message passing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph structured prediction energy networks")
  Member "efficiently searching for frustrated cycles in map inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "graph structured prediction energy networks")
  Member "continuous multiclass labeling approaches and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "image labeling based on graphical models using wasserstein messages and geometric assignment")
  Member "generalized roof duality and bisubmodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "higher order maximum persistency and comparison theorems")

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)