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: "decision forests convolutional networks and the models in between"
  Abstract: This paper investigates the connections between two state of the art classifiers: decision forests (DFs, including decision jungles) and convolutional neural networks (CNNs). Decision forests are computationally efficient thanks to their conditional computation property (computation is confined to only a small region of the tree, the nodes along a single branch). CNNs achieve state of the art accuracy, thanks to their representation learning capabilities. We present a systematic analysis of how to fuse conditional computation with representation learning and achieve a continuum of hybrid models with different ratios of accuracy vs. efficiency. We call this new family of hybrid models conditional networks. Conditional networks can be thought of as: i) decision trees augmented with data transformation operators, or ii) CNNs, with block-diagonal sparse weight matrices, and explicit data routing functions. Experimental validation is performed on the common task of image classification on both the CIFAR and Imagenet datasets. Compared to state of the art CNNs, our hybrid models yield the same accuracy with a fraction of the compute cost and much smaller number of parameters.

Connected through hyperedge HE-1 (induced by source paper "interpretation and simplification of deep forest"):
  with members:
    - "searching for mobilenetv3"
    - "explainable machine learning for scientific insights and discoveries"
    - "multi layered gradient boosting decision trees"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "methods for interpreting and understanding deep neural networks"
    - "deep learning a critical appraisal"
    - "explainable and explicit visual reasoning over scene graphs"

Connected through hyperedge HE-2 (induced by source paper "the tree ensemble layer differentiability meets conditional computation"):
  with members:
    - "adaptive neural trees"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "learning deep nearest neighbor representations using differentiable boundary trees"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "xgboost a scalable tree boosting system"
    - "conditional computation in neural networks for faster models"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "neural rule ensembles encoding sparse feature interactions into neural networks"):
  with members:
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"
    - "adam a method for stochastic optimization"
    - "interpreting tree ensembles with intrees"

Connected through hyperedge HE-4 (induced by source paper "a survey of techniques all classifiers can learn from deep networks models optimizations and regularization"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "multi layered gradient boosting decision trees"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "sampling strategies in siamese networks for unsupervised speech representation learning"
    - "deep neural decision trees"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "embedding visual hierarchy with deep networks for large scale visual recognition"

Connected through hyperedge HE-5 (induced by source paper "techniques all classifiers can learn from deep networks models optimizations and regularization"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "multi layered gradient boosting decision trees"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "sampling strategies in siamese networks for unsupervised speech representation learning"
    - "deep neural decision trees"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "autoaugment learning augmentation policies from data"
    - "deep forest"

Connected through hyperedge HE-6 (induced by source paper "training decision trees as replacement for convolution layers"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "splinenets continuous neural decision graphs"
    - "style aggregated network for facial landmark detection"
    - "neural ordinary differential equations"
    - "conditional information gain networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deep convolutional decision jungle for image classification"

Connected through hyperedge HE-7 (induced by source paper "tree gated deep mixture of experts for pose robust face alignment"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "tree gated deep regressor ensemble for face alignment in the wild"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "adaptive neural trees"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "fine grained head pose estimation without keypoints"
    - "pose invariant face alignment with a single cnn"
    - "improving landmark localization with semi supervised learning"

Connected through hyperedge HE-8 (induced by source paper "tree gated deep regressor ensemble for face alignment in the wild"):
  with members:
    - "adaptive neural trees"
    - "face alignment robust to pose expressions and occlusions"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "pose invariant face alignment with a single cnn"
    - "improving landmark localization with semi supervised learning"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "adam a method for stochastic optimization"
    - "learning factored representations in a deep mixture of experts"

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.CV(Computer Vision and Pattern Recognition)