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: "learning graphical model parameters with approximate marginal inference"
  Abstract: Likelihood-based learning of graphical models faces challenges of computational complexity and robustness to model misspecification. This paper studies methods that fit parameters directly to maximize a measure of the accuracy of predicted marginals, taking into account both model and inference approximations at training time. Experiments on imaging problems suggest marginalization-based learning performs better than likelihood-based approximations on difficult problems where the model being fit is approximate in nature.

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 "lp sparsemap differentiable relaxed optimization for sparse structured prediction"):
  with members:
    - "learning latent trees with stochastic perturbations and differentiable dynamic programming"
    - "sparse sequence to sequence models"
    - "differentiable convex optimization layers"
    - "meta learning with implicit gradients"
    - "listops a diagnostic dataset for latent tree learning"
    - "backpropagating through structured argmax using a spigot"
    - "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"
    - "structured attention networks"

Connected through hyperedge HE-3 (induced by source paper "a bayesian filter for multi view 3d multi object tracking with occlusion handling"):
  with members:
    - "deep multi camera people detection"
    - "deep occlusion reasoning for multi camera multi target detection"
    - "yolo9000 better faster stronger"
    - "performance measures and a data set for multi target multi camera tracking"
    - "you only look once unified real time object detection"
    - "salsa a novel dataset for multimodal group behavior analysis"
    - "fast r cnn"
    - "high speed tracking with kernelized correlation filters"

Connected through hyperedge HE-4 (induced by source paper "autoregressive text generation beyond feedback loops"):
  with members:
    - "on accurate evaluation of gans for language generation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "large scale classification of structured objects using a crf with deep class embedding"
    - "deep state space models for unconditional word generation"
    - "language gans falling short"
    - "dp gan diversity promoting generative adversarial network for generating informative and diversified text"
    - "structured attention networks"
    - "advances in variational inference"

Connected through hyperedge HE-5 (induced by source paper "deepigeos a deep interactive geodesic framework for medical image segmentation"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "a survey on deep learning in medical image analysis"
    - "end to end tracking and semantic segmentation using recurrent neural networks"
    - "instance sensitive fully convolutional networks"
    - "temporal convolutional networks for action segmentation and detection"
    - "deepcut object segmentation from bounding box annotations using convolutional neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-6 (induced by source paper "fast and differentiable message passing for stereo vision"):
  with members:
    - "ga net guided aggregation net for end to end stereo matching"
    - "generalized range moves"
    - "memory efficient max flow for multi label submodular mrfs"
    - "embedded real time stereo estimation via semi global matching on the gpu"
    - "conditional random fields as recurrent neural networks"
    - "iteratively reweighted graph cut for multi label mrfs with non convex priors"
    - "loopy belief propagation for approximate inference an empirical study"

Connected through hyperedge HE-7 (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-8 (induced by source paper "generative scatternet hybrid deep learning g shdl network with structural priors for semantic image segmentation"):
  with members:
    - "texture and structure incorporated scatternet hybrid deep learning network ts shdl for brain matter segmentation"
    - "discriminative training of deep fully connected continuous crfs with task specific loss"
    - "dual tree wavelet scattering network with parametric log transformation for object classification"
    - "scatternet hybrid deep learning shdl network for object classification"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "multi path feedback recurrent neural network for scene parsing"
    - "multi resolution dual tree wavelet scattering network for signal classification"
    - "pcanet a simple deep learning baseline for image classification"

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)