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 comparative study of modern inference techniques for structured discrete energy minimization problems"):
  Abstract: Szeliski et al. published an influential study in 2006 on energy minimization methods for Markov Random Fields (MRF). This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenomenal success of random field models means that the kinds of inference problems that have to be solved changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large la\-bel-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of 32 state-of-the-art optimization techniques on a corpus of 2,453 energy minimization instances from diverse applications in computer vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.

Includes members:
  Member "on measure concentration of random maximum a posteriori perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning with differentiable perturbed optimizers")
  Member "efficient mrf energy minimization via adaptive diminishing smoothing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "map inference via l2 sphere linear program reformulation")
  Member "opengm a c library for discrete graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "referring expression grounding by marginalizing scene graph likelihood")
  Member "getting feasible variable estimates from infeasible ones mrf local polytope study":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "maximum persistency via iterative relaxed inference with graphical models")
  Member "efficiently searching for frustrated cycles in map inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph structured prediction energy networks")
  Member "submodular decomposition framework for inference in associative markov networks with global constraints":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "submodular relaxation for inference in markov random fields")
  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")

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)