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: "dope distributed optimization for pairwise energies"
  Abstract: We formulate an Alternating Direction Method of Mul-tipliers (ADMM) that systematically distributes the computations of any technique for optimizing pairwise functions, including non-submodular potentials. Such discrete functions are very useful in segmentation and a breadth of other vision problems. Our method decomposes the problem into a large set of small sub-problems, each involving a sub-region of the image domain, which can be solved in parallel. We achieve consistency between the sub-problems through a novel constraint that can be used for a large class of pair-wise functions. We give an iterative numerical solution that alternates between solving the sub-problems and updating consistency variables, until convergence. We report comprehensive experiments, which demonstrate the benefit of our general distributed solution in the case of the popular serial algorithm of Boykov and Kolmogorov (BK algorithm) and, also, in the context of non-submodular functions.

Connected through hyperedge HE-1 (induced by source paper "discretely constrained deep network for weakly supervised segmentation"):
  with members:
    - "prior aware neural network for partially supervised multi organ segmentation"
    - "curriculum semi supervised segmentation"
    - "constrained cnn losses for weakly supervised segmentation"
    - "beyond gradient descent for regularized segmentation losses"
    - "predicting deeper into the future of semantic segmentation"
    - "unbiased shape compactness for segmentation"
    - "discrete continuous admm for transductive inference in higher order mrfs"
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-2 (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"
    - "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"
    - "training neural networks without gradients a scalable admm approach"

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)