human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "worst case polynomial time exact map inference on discrete models with global dependencies"):
  with members:
    - "optimizing for measure of performance in max margin parsing"
    - "a compact linear programming relaxation for binary sub modular mrf"
    - "tighter linear program relaxations for high order graphical models"
    - "discriminative probabilistic models for relational data"
    - "fast exact inference for recursive cardinality models"
    - "a tutorial on dual decomposition and lagrangian relaxation for inference in natural language processing"
    - "tightening lp relaxations for map using message passing"
    - "complexity of inference in graphical models"

Connected through hyperedge HE-2 (induced by source paper "simulating crf with cnn for cnn"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "end to end training of hybrid cnn crf models for stereo"
    - "fast exact and multi scale inference for semantic image segmentation with deep gaussian crfs"
    - "computing the stereo matching cost with a convolutional neural network"
    - "higher order potentials in end to end trainable conditional random fields"
    - "learning sparse high dimensional filters image filtering dense crfs and bilateral neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "bottleneck potentials in markov random fields"):
  with members:
    - "map inference via block coordinate frank wolfe algorithm"
    - "solving dense image matching in real time using discrete continuous optimization"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "learning to compare image patches via convolutional neural networks"
    - "probabilistic intra retinal layer segmentation in 3 d oct images using global shape regularization"
    - "combining the shortest paths and the bottleneck paths problems"

Connected through hyperedge HE-4 (induced by source paper "map inference via l2 sphere linear program reformulation"):
  with members:
    - "bethe admm for tree decomposition based parallel map inference"
    - "efficient mrf energy minimization via adaptive diminishing smoothing"
    - "efficiently searching for frustrated cycles in map inference"
    - "lagrangian relaxation for map estimation in graphical models"

Connected through hyperedge HE-5 (induced by source paper "unsupervised assignment flow label learning on feature manifolds by spatially regularized geometric assignment"):
  with members:
    - "image labeling by assignment"
    - "sparse coding on symmetric positive definite manifolds using bregman divergences"

Connected through hyperedge HE-6 (induced by source paper "deep structured prediction with nonlinear output transformations"):
  with members:
    - "learning approximate inference networks for structured prediction"
    - "searnn training rnns with global local losses"
    - "input convex neural networks"
    - "pixel recurrent neural networks"
    - "structured prediction energy networks"
    - "deeply learning the messages in message passing inference"
    - "deep residual learning for image recognition"
    - "fully connected deep structured networks"

Connected through hyperedge HE-7 (induced by source paper "beyond gradient descent for regularized segmentation losses"):
  with members:
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "efficient graph cut optimization for full crfs with quantized edges"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "unbiased shape compactness for segmentation"
    - "discrete continuous admm for transductive inference in higher order mrfs"
    - "dope distributed optimization for pairwise energies"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-8 (induced by source paper "accelerated inference in markov random fields via smooth riemannian optimization"):
  with members:
    - "bonnet an open source training and deployment framework for semantic segmentation in robotics using cnns"
    - "large scale binary quadratic optimization using semidefinite relaxation and applications"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "a survey of semantic segmentation"
    - "beyond pixels a comprehensive survey from bottom up to semantic image segmentation and cosegmentation"
    - "efficient sdp inference for fully connected crfs based on low rank decomposition"

Question: Please tell me which class the center node 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)