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: "constrained deep learning using conditional gradient and applications in computer vision"
  Abstract: A number of results have recently demonstrated the benefits of incorporating various constraints when training deep architectures in vision and machine learning. The advantages range from guarantees for statistical generalization to better accuracy to compression. But support for general constraints within widely used libraries remains scarce and their broader deployment within many applications that can benefit from them remains under-explored. Part of the reason is that Stochastic gradient descent (SGD), the workhorse for training deep neural networks, does not natively deal with constraints with global scope very well. In this paper, we revisit a classical first order scheme from numerical optimization, Conditional Gradients (CG), that has, thus far had limited applicability in training deep models. We show via rigorous analysis how various constraints can be naturally handled by modifications of this algorithm. We provide convergence guarantees and show a suite of immediate benefits that are possible -- from training ResNets with fewer layers but better accuracy simply by substituting in our version of CG to faster training of GANs with 50% fewer epochs in image inpainting applications to provably better generalization guarantees using efficiently implementable forms of recently proposed regularizers.

Connected through hyperedge HE-1 (induced by source paper "the empirical duality gap of constrained statistical learning"):
  with members:
    - "an approximation algorithm for risk averse submodular optimization"
    - "learning one hidden layer neural networks with landscape design"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-2 (induced by source paper "constrained cnn losses for weakly supervised segmentation"):
  with members:
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "imposing hard constraints on deep networks promises and limitations"
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "constrained deep networks lagrangian optimization via log barrier extensions"):
  with members:
    - "constrained cnn losses for weakly supervised segmentation"
    - "a curriculum domain adaptation approach to the semantic segmentation of urban scenes"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "beyond gradient descent for regularized segmentation losses"
    - "imposing hard constraints on deep networks promises and limitations"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "quantized frank wolfe faster optimization lower communication and projection free"):
  with members:
    - "deep frank wolfe for neural network optimization"
    - "a distributed frank wolfe framework for learning low rank matrices with the trace norm"
    - "neural conditional gradients"
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "learning infinite rbms with frank wolfe"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "qsgd communication efficient sgd via gradient quantization and encoding"

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.LG(Machine Learning)