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 "multicuts and perturb map for probabilistic graph clustering"):
  Abstract: We present a probabilistic graphical model formulation for the graph clustering problem. This enables to locally represent uncertainty of image partitions by approximate marginal distributions in a mathematically substantiated way, and to rectify local data term cues so as to close contours and to obtain valid partitions. #R##N#We exploit recent progress on globally optimal MAP inference by integer programming and on perturbation-based approximations of the log-partition function, in order to sample clusterings and to estimate marginal distributions of node-pairs both more accurately and more efficiently than state-of-the-art methods. Our approach works for any graphically represented problem instance. This is demonstrated for image segmentation and social network cluster analysis. Our mathematical ansatz should be relevant also for other combinatorial problems.

Includes members:
  Member "a comparative study of modern inference techniques for structured discrete energy minimization problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "worst case polynomial time exact map inference on discrete models with global dependencies")
  Member "higher order segmentation via multicuts":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "moral lineage tracing")
  Member "on sampling from the gibbs distribution with random maximum a posteriori perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning with differentiable perturbed optimizers")
  Member "opengm a c library for discrete graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "referring expression grounding by marginalizing scene graph likelihood")
  Member "on the partition function and random maximum a posteriori perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning with differentiable perturbed optimizers")
  Member "large scale correlation clustering optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "digital technologies in airport ground operations")
  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 "a tutorial on spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximating spectral clustering via sampling a review")

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)