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 "coarse to fine lifted map inference in computer vision"):
  Abstract: There is a vast body of theoretical research on lifted inference in probabilistic graphical models (PGMs). However, few demonstrations exist where lifting is applied in conjunction with top of the line applied algorithms. We pursue the applicability of lifted inference for computer vision (CV), with the insight that a globally optimal (MAP) labeling will likely have the same label for two symmetric pixels. The success of our approach lies in efficiently handling a distinct unary potential on every node (pixel), typical of CV applications. This allows us to lift the large class of algorithms that model a CV problem via PGM inference. We propose a generic template for coarse-to-fine (C2F) inference in CV, which progressively refines an initial coarsely lifted PGM for varying quality-time trade-offs. We demonstrate the performance of C2F inference by developing lifted versions of two near state-of-the-art CV algorithms for stereo vision and interactive image segmentation. We find that, against flat algorithms, the lifted versions have a much superior anytime performance, without any loss in final solution quality.

Includes members:
  Member "learning tractable probabilistic models for fault localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "visualizing and understanding sum product networks")
  Member "a comparative study of modern inference techniques for structured discrete energy minimization problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "worst case polynomial time exact map inference on discrete models with global dependencies")
  Member "lifted probabilistic inference for asymmetric graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generating and sampling orbits for lifted probabilistic inference")
  Member "on the complexity and approximation of binary evidence in lifted inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "taming reasoning in temporal probabilistic relational models")
  Member "rockit exploiting parallelism and symmetry for map inference in statistical relational models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "beyond markov logic efficient mining of prediction rules in large graphs")
  Member "markov chains on orbits of permutation groups":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generating and sampling orbits for lifted probabilistic inference")
  Member "counting belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lifted hybrid variational inference")
  Member "lifted graphical models a survey":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep adaptive semantic logic dasl compiling declarative knowledge into deep neural networks")

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)