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: "beyond gradient descent for regularized segmentation losses"
  Abstract: The simplicity of gradient descent (GD) made it the default method for training ever-deeper and complex neural networks. Both loss functions and architectures are often explicitly tuned to be amenable to this basic local optimization. In the context of weakly-supervised CNN segmentation, we demonstrate a well-motivated loss function where an alternative optimizer (ADM) achieves the state-of-the-art while GD performs poorly. Interestingly, GD obtains its best result for a "smoother" tuning of the loss function. The results are consistent across different network architectures. Our loss is motivated by well-understood MRF/CRF regularization models in "shallow" segmentation and their known global solvers. Our work suggests that network design/training should pay more attention to optimization methods.

Connected through hyperedge HE-1 (induced by source paper "belief propagation reloaded learning bp layers for labeling problems"):
  with members:
    - "hierarchical deep stereo matching on high resolution images"
    - "ga net guided aggregation net for end to end stereo matching"
    - "learned collaborative stereo refinement"
    - "learning depth with convolutional spatial propagation network"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "stereonet guided hierarchical refinement for real time edge aware depth prediction"

Connected through hyperedge HE-2 (induced by source paper "gated crf loss for weakly supervised semantic image segmentation"):
  with members:
    - "rethinking imagenet pre training"
    - "towards closing the gap in weakly supervised semantic segmentation with dcnns combining local and global models"
    - "convolutional crfs for semantic segmentation"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "efficient relaxations for dense crfs with sparse higher order potentials"
    - "on regularized losses for weakly supervised cnn segmentation"

Connected through hyperedge HE-3 (induced by source paper "efficient segmentation learning downsampling near semantic boundaries"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "active convolution learning the shape of convolution for image classification"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deformable convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "differentiation of blackbox combinatorial solvers"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "guiding high performance sat solvers with unsat core predictions"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "smart predict and optimize for hard combinatorial optimization problems"
    - "end to end learning and optimization on graphs"
    - "learning a sat solver from single bit supervision"

Connected through hyperedge HE-5 (induced by source paper "constrained deep networks lagrangian optimization via log barrier extensions"):
  with members:
    - "constrained cnn losses for weakly supervised segmentation"
    - "constrained deep learning using conditional gradient and applications in computer vision"
    - "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"
    - "imposing hard constraints on deep networks promises and limitations"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-6 (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"
    - "predicting deeper into the future of semantic segmentation"
    - "unbiased shape compactness for segmentation"
    - "discrete continuous admm for transductive inference in higher order mrfs"
    - "dope distributed optimization for pairwise energies"
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "differentiable combinatorial losses through generalized gradients of linear programs"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "differentiable convex optimization layers"
    - "multi stage pretraining for abstractive summarization"
    - "improving sequence to sequence learning via optimal transport"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "optnet differentiable optimization as a layer in neural networks"

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)