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: "piecewise training for undirected models"
  Abstract: For many large undirected models that arise in real-world applications, exact maximumlikelihood training is intractable, because it requires computing marginal distributions of the model. Conditional training is even more difficult, because the partition function depends not only on the parameters, but also on the observed input, requiring repeated inference over each training example. An appealing idea for such models is to independently train a local undirected classifier over each clique, afterwards combining the learned weights into a single global model. In this paper, we show that this piecewise method can be justified as minimizing a new family of upper bounds on the log partition function. On three natural-language data sets, piecewise training is more accurate than pseudolikelihood, and often performs comparably to global training using belief propagation.

Connected through hyperedge HE-1 (induced by source paper "learning undirected models via query training"):
  with members:
    - "to relieve your headache of training an mrf take advil"
    - "adversarial variational inference and learning in markov random fields"
    - "neural variational inference and learning in undirected graphical models"

Connected through hyperedge HE-2 (induced by source paper "a parametric top view representation of complex road scenes"):
  with members:
    - "learning to look around objects for top view representations of outdoor scenes"
    - "learning to adapt structured output space for semantic segmentation"
    - "layer structured 3d scene inference via view synthesis"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "playing for benchmarks"

Connected through hyperedge HE-3 (induced by source paper "large scale classification of structured objects using a crf with deep class embedding"):
  with members:
    - "deep feature based contextual model for object detection"
    - "yolo9000 better faster stronger"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "bidirectional lstm crf models for sequence tagging"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fully connected deep structured networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-4 (induced by source paper "amortized inference and learning in latent conditional random fields for weakly supervised semantic image segmentation"):
  with members:
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "bottom up top down cues for weakly supervised semantic segmentation"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "built in foreground background prior for weakly supervised semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"

Connected through hyperedge HE-5 (induced by source paper "learning to segment with image level supervision"):
  with members:
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "conditional random fields as recurrent neural networks"
    - "multi scale context aggregation by dilated convolutions"
    - "fully connected deep structured networks"

Connected through hyperedge HE-6 (induced by source paper "conditional random field and deep feature learning for hyperspectral image segmentation"):
  with members:
    - "learning aerial image segmentation from online maps"
    - "structured feature learning for pose estimation"
    - "crf cnn modeling structured information in human pose estimation"
    - "crf learning with cnn features for image segmentation"
    - "joint training of generic cnn crf models with stochastic optimization"
    - "closed form training of conditional random fields for large scale image segmentation"
    - "convolutional neural networks at constrained time cost"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"

Connected through hyperedge HE-7 (induced by source paper "exploring context with deep structured models for semantic segmentation"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "semantic image segmentation via deep parsing network"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "conditional random fields as recurrent neural networks"
    - "multi scale context aggregation by dilated convolutions"
    - "fully connected deep structured networks"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "efficient piecewise training of deep structured models for semantic segmentation"):
  with members:
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "semantic image segmentation via deep parsing network"
    - "conditional random fields as recurrent neural networks"
    - "fully connected deep structured networks"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"
    - "learning deconvolution network for semantic segmentation"
    - "convolutional feature masking for joint object and stuff segmentation"
    - "closed form training of conditional random fields for large scale image segmentation"

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)