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 deep structured models"
  Abstract: Many problems in real-world applications involve predicting several random variables which are statistically related. Markov random fields (MRFs) are a great mathematical tool to encode such relationships. The goal of this paper is to combine MRFs with deep learning algorithms to estimate complex representations while taking into account the dependencies between the output random variables. Towards this goal, we propose a training algorithm that is able to learn structured models jointly with deep features that form the MRF potentials. Our approach is efficient as it blends learning and inference and makes use of GPU acceleration. We demonstrate the effectiveness of our algorithm in the tasks of predicting words from noisy images, as well as multi-class classification of Flickr photographs. We show that joint learning of the deep features and the MRF parameters results in significant performance gains.

Connected through hyperedge HE-1 (induced by source paper "relational neural machines"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "neural logic machines"
    - "lyrics a general interface layer to integrate ai and deep learning"
    - "neural logic reinforcement learning"
    - "neural markov logic networks"
    - "learning relational representations with auto encoding logic programs"
    - "deepproblog neural probabilistic logic programming"
    - "on a convex logic fragment for learning and reasoning"

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 "adversarial structure matching for structured prediction tasks"):
  with members:
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "taskonomy disentangling task transfer learning"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "regularizing deep networks by modeling and predicting label structure"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "deep value networks learn to evaluate and iteratively refine structured outputs"
    - "interpreting deep visual representations via network dissection"
    - "learning affinity via spatial propagation networks"

Connected through hyperedge HE-4 (induced by source paper "differentiation of blackbox combinatorial solvers"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "guiding high performance sat solvers with unsat core predictions"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "smart predict and optimize for hard combinatorial optimization problems"
    - "end to end learning and optimization on graphs"
    - "learning a sat solver from single bit supervision"

Connected through hyperedge HE-5 (induced by source paper "orderless recurrent models for multi label classification"):
  with members:
    - "elucidating image to set prediction an analysis of models losses and datasets"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "a comprehensive survey of deep learning for image captioning"
    - "averaging weights leads to wider optima and better generalization"
    - "multi label image classification via knowledge distillation from weakly supervised detection"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "learning spatial regularization with image level supervisions for multi label image classification"

Connected through hyperedge HE-6 (induced by source paper "frank wolfe network an interpretable deep structure for non sparse coding"):
  with members:
    - "learning simple thresholded features with sparse support recovery"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "a unified optimization view on generalized matching pursuit and frank wolfe"
    - "maximal sparsity with deep networks"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "normalizing the normalizers comparing and extending network normalization schemes"

Connected through hyperedge HE-7 (induced by source paper "pixel adaptive convolutional neural networks"):
  with members:
    - "image transformer"
    - "convolutional crfs for semantic segmentation"
    - "splatnet sparse lattice networks for point cloud processing"
    - "fast end to end trainable guided filter"
    - "segmentation aware convolutional networks using local attention masks"
    - "non local neural networks"
    - "attention is all you need"
    - "learning affinity via spatial propagation networks"

Connected through hyperedge HE-8 (induced by source paper "learning propagation for arbitrarily structured data"):
  with members:
    - "surfconv bridging 3d and 2d convolution for rgbd images"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "a simple baseline algorithm for graph classification"
    - "splatnet sparse lattice networks for point cloud processing"
    - "fast end to end trainable guided filter"
    - "videos as space time region graphs"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "tangent convolutions for dense prediction in 3d"

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)