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: "multi layered gradient boosting decision trees"
  Abstract: Multi-layered representation is believed to be the key ingredient of deep neural networks especially in cognitive tasks like computer vision. While non-differentiable models such as gradient boosting decision trees (GBDTs) are the dominant methods for modeling discrete or tabular data, they are hard to incorporate with such representation learning ability. In this work, we propose the multi-layered GBDT forest (mGBDTs), with an explicit emphasis on exploring the ability to learn hierarchical representations by stacking several layers of regression GBDTs as its building block. The model can be jointly trained by a variant of target propagation across layers, without the need to derive back-propagation nor differentiability. Experiments and visualizations confirmed the effectiveness of the model in terms of performance and representation learning ability.

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"
    - "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"
    - "facial expression recognition using enhanced deep 3d convolutional neural networks"

Connected through hyperedge HE-2 (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"
    - "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"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-3 (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"
    - "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"
    - "adaptive neural trees"

Connected through hyperedge HE-4 (induced by source paper "robust decision trees against adversarial examples"):
  with members:
    - "gradient regularized budgeted boosting"
    - "theoretically principled trade off between robustness and accuracy"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "scaling provable adversarial defenses"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "mixtrain scalable training of formally robust neural networks"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "neural forest learning"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "adaptive neural trees"
    - "deep forest towards an alternative to deep neural networks"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "mask r cnn"
    - "bilinear cnn models for fine grained visual recognition"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-6 (induced by source paper "neural oblivious decision ensembles for deep learning on tabular data"):
  with members:
    - "sparse sequence to sequence models"
    - "deep neural decision trees"
    - "quasi hyperbolic momentum and adam for deep learning"
    - "sparsemax and relaxed wasserstein for topic sparsity"
    - "averaging weights leads to wider optima and better generalization"
    - "catboost unbiased boosting with categorical features"
    - "deep forest towards an alternative to deep neural networks"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"

Connected through hyperedge HE-7 (induced by source paper "stacked autoencoder based deep random vector functional link neural network for classification"):
  with members:
    - "random vector functional link neural network based ensemble deep learning"
    - "enhancing multi class classification of random forest using random vector functional neural network and oblique decision surfaces"
    - "a vest of the pseudoinverse learning algorithm"
    - "self normalizing neural networks"
    - "densely connected convolutional networks"
    - "training neural networks without gradients a scalable admm approach"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "training robust tree ensembles for security"):
  with members:
    - "detecting and characterizing lateral phishing at scale"
    - "gradient regularized budgeted boosting"
    - "robust decision trees against adversarial examples"
    - "robustness verification of tree based models"
    - "adgraph a graph based approach to ad and tracker blocking"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "xgboost a scalable tree boosting system"
    - "evasion and hardening of tree ensemble classifiers"

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)