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: "modeling latent variable uncertainty for loss based learning"
  Abstract: We consider the problem of parameter estimation using weakly supervised datasets, where a training sample consists of the input and a partially specified annotation, which we refer to as the output. The missing information in the annotation is modeled using latent variables. Previous methods overburden a single distribution with two separate tasks: (i) modeling the uncertainty in the latent variables during training; and (ii) making accurate predictions for the output and the latent variables during testing. We propose a novel framework that separates the demands of the two tasks using two distributions: (i) a conditional distribution to model the uncertainty of the latent variables for a given input-output pair; and (ii) a delta distribution to predict the output and the latent variables for a given input. During learning, we encourage agreement between the two distributions by minimizing a loss-based dissimilarity coefficient. Our approach generalizes latent SVM in two important ways: (i) it models the uncertainty over latent variables instead of relying on a pointwise estimate; and (ii) it allows the use of loss functions that depend on latent variables, which greatly increases its applicability. We demonstrate the efficacy of our approach on two challenging problems---object detection and action detection---using publicly available datasets.

Connected through hyperedge HE-1 (induced by source paper "dissimilarity coefficient based weakly supervised object detection"):
  with members:
    - "zigzag learning for weakly supervised object detection"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "learning human poses from actions"
    - "collaborative learning for weakly supervised object detection"
    - "deep self taught learning for weakly supervised object localization"
    - "multiple instance curriculum learning for weakly supervised object detection"
    - "saliency guided end to end learning for weakly supervised object detection"
    - "weakly supervised object localization with multi fold multiple instance learning"

Connected through hyperedge HE-2 (induced by source paper "learning human poses from actions"):
  with members:
    - "multi context attention for human pose estimation"
    - "stacked hourglass networks for human pose estimation"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "pose for action action for pose"
    - "constrained convolutional neural networks for weakly supervised segmentation"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "fully convolutional multi class multiple instance learning"
    - "efficient structured prediction with latent variables for general graphical models"

Connected through hyperedge HE-3 (induced by source paper "disco nets dissimilarity coefficient networks"):
  with members:
    - "training a feedback loop for hand pose estimation"
    - "generative adversarial text to image synthesis"
    - "attribute2image conditional image generation from visual attributes"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "generative moment matching networks"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "hands deep in deep learning for hand pose estimation"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-4 (induced by source paper "generalized majorization minimization"):
  with members:
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "spotlight the negatives a generalized discriminative latent model"
    - "on learning to localize objects with minimal supervision"
    - "on the convergence of bound optimization algorithms"

Connected through hyperedge HE-5 (induced by source paper "on learning to localize objects with minimal supervision"):
  with members:
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "convex and scalable weakly labeled svms"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "unsupervised discovery of mid level discriminative patches"
    - "a convex relaxation for weakly supervised classifiers"
    - "optimization with sparsity inducing penalties"

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)