human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "normalized cut loss for weakly supervised cnn segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weakly supervised salient object detection via scribble annotations")
  Member "efficient graph cut optimization for full crfs with quantized edges":
    also connected through other hyperedges:
      (none)
  Member "on regularized losses for weakly supervised cnn segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weakly supervised salient object detection via scribble annotations")
  Member "unbiased shape compactness for segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discretely constrained deep network for weakly supervised segmentation")
  Member "discrete continuous admm for transductive inference in higher order mrfs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discretely constrained deep network for weakly supervised segmentation")
  Member "dope distributed optimization for pairwise energies":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discretely constrained deep network for weakly supervised segmentation")
  Member "seed expand and constrain three principles for weakly supervised image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relationship embedded representation learning for grounding referring expressions")

Question: Please tell me which class the source paper 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)