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: "efficient linear programming for dense crfs"
  Abstract: The fully connected conditional random field (CRF) with Gaussian pairwise potentials has proven popular and effective for multi-class semantic segmentation. While the energy of a dense CRF can be minimized accurately using a linear programming (LP) relaxation, the state-of-the-art algorithm is too slow to be useful in practice. To alleviate this deficiency, we introduce an efficient LP minimization algorithm for dense CRFs. To this end, we develop a proximal minimization framework, where the dual of each proximal problem is optimized via block coordinate descent. We show that each block of variables can be efficiently optimized. Specifically, for one block, the problem decomposes into significantly smaller subproblems, each of which is defined over a single pixel. For the other block, the problem is optimized via conditional gradient descent. This has two advantages: 1) the conditional gradient can be computed in a time linear in the number of pixels and labels; and 2) the optimal step size can be computed analytically. Our experiments on standard datasets provide compelling evidence that our approach outperforms all existing baselines including the previous LP based approach for dense CRFs.

Connected through hyperedge HE-1 (induced by source paper "proximal mean field for neural network quantization"):
  with members:
    - "proxquant quantized neural networks via proximal operators"
    - "a survey on methods and theories of quantized neural networks"
    - "binaryrelax a relaxation approach for training deep neural networks with quantized weights"
    - "snip single shot network pruning based on connection sensitivity"
    - "wrpn wide reduced precision networks"
    - "snapshot ensembles train 1 get m for free"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "loss aware binarization of deep networks"

Connected through hyperedge HE-2 (induced by source paper "on regularized losses for weakly supervised cnn segmentation"):
  with members:
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "adversarial learning for semi supervised semantic segmentation"
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "efficient continuous relaxations for dense crf"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "deepcut object segmentation from bounding box annotations using convolutional neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "map inference via block coordinate frank wolfe algorithm"):
  with members:
    - "minding the gaps for block frank wolfe optimization of structured svms"
    - "a dual ascent framework for lagrangean decomposition of combinatorial problems"
    - "a new look at reweighted message passing"
    - "submodular relaxation for inference in markov random fields"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "a multi plane block coordinate frank wolfe algorithm for training structural svms with a costly max oracle"
    - "block coordinate frank wolfe optimization for structural svms"
    - "lagrangian relaxation for map estimation in graphical models"

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)