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: "efficient structured prediction with latent variables for general graphical models"
  Abstract: In this paper we propose a unified framework for structured prediction with latent variables which includes hidden conditional random fields and latent structured support vector machines as special cases. We describe a local entropy approximation for this general formulation using duality, and derive an efficient message passing algorithm that is guaranteed to converge. We demonstrate its effectiveness in the tasks of image segmentation as well as 3D indoor scene understanding from single images, showing that our approach is superior to latent structured support vector machines and hidden conditional random fields.

Connected through hyperedge HE-1 (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"
    - "modeling latent variable uncertainty for loss based learning"

Connected through hyperedge HE-2 (induced by source paper "smooth loss functions for deep top k classification"):
  with members:
    - "analysis and optimization of loss functions for multiclass top k and multilabel classification"
    - "deep residual learning for image recognition"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "aligning books and movies towards story like visual explanations by watching movies and reading books"):
  with members:
    - "don t just listen use your imagination leveraging visual common sense for non visual tasks"
    - "skip thought vectors"
    - "a dataset for movie description"
    - "show attend and tell neural image caption generation with visual attention"
    - "what s cookin interpreting cooking videos using text speech and vision"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "unifying visual semantic embeddings with multimodal neural language models"

Connected through hyperedge HE-4 (induced by source paper "learning deep structured models"):
  with members:
    - "conditional random fields as recurrent neural networks"
    - "fully connected deep structured networks"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "simultaneous detection and segmentation"
    - "structured learning via logistic regression"
    - "caffe convolutional architecture for fast feature embedding"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "multi utility learning structured output learning with multiple annotation specific loss functions"):
  with members:
    - "structured learning from partial annotations"

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)