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: "deep forest"
  Abstract: Current deep learning models are mostly build upon neural networks, i.e., multiple layers of parameterized differentiable nonlinear modules that can be trained by backpropagation. In this paper, we explore the possibility of building deep models based on non-differentiable modules. We conjecture that the mystery behind the success of deep neural networks owes much to three characteristics, i.e., layer-by-layer processing, in-model feature transformation and sufficient model complexity. We propose the gcForest approach, which generates \textit{deep forest} holding these characteristics. This is a decision tree ensemble approach, with much less hyper-parameters than deep neural networks, and its model complexity can be automatically determined in a data-dependent way. Experiments show that its performance is quite robust to hyper-parameter settings, such that in most cases, even across different data from different domains, it is able to get excellent performance by using the same default setting. This study opens the door of deep learning based on non-differentiable modules, and exhibits the possibility of constructing deep models without using backpropagation.

Connected through hyperedge HE-1 (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"
    - "adaptive neural trees"

Connected through hyperedge HE-2 (induced by source paper "conversion rate prediction via post click behaviour modeling"):
  with members:
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "behavior sequence transformer for e commerce recommendation in alibaba"
    - "sdm sequential deep matching model for online large scale recommender system"
    - "deep session interest network for click through rate prediction"
    - "rank and rate multi task learning for recommender systems"
    - "optimized cost per click in taobao display advertising"
    - "deep forest towards an alternative to deep neural networks"
    - "click through rate prediction for contextual advertisment using linear regression"

Connected through hyperedge HE-3 (induced by source paper "resource allocation using gradient boosting aided deep q network for iot in c rans"):
  with members:
    - "bit level optimized neural network for multi antenna channel quantization"
    - "learning to optimize training deep neural networks for interference management"
    - "an introduction to deep reinforcement learning"
    - "mimo channel information feedback using deep recurrent network"
    - "model driven deep learning for physical layer communications"
    - "deep forest towards an alternative to deep neural networks"
    - "xgboost a scalable tree boosting system"
    - "user association in 5g networks a survey and an outlook"

Connected through hyperedge HE-4 (induced by source paper "multi label learning with deep forest"):
  with members:
    - "distributed deep forest and its application to automatic detection of cash out fraud"
    - "deep forest towards an alternative to deep neural networks"
    - "autoencoder by forest"
    - "a unified view of multi label performance measures"
    - "cnn rnn a unified framework for multi label image classification"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "large scale multi label text classification revisiting neural networks"
    - "top down induction of clustering trees"

Connected through hyperedge HE-5 (induced by source paper "forest representation learning guided by margin distribution"):
  with members:
    - "multi layered gradient boosting decision trees"
    - "deep forest towards an alternative to deep neural networks"
    - "learning deep resnet blocks sequentially using boosting theory"
    - "autoencoder by forest"
    - "xgboost a scalable tree boosting system"
    - "on the doubt about margin explanation of boosting"

Connected through hyperedge HE-6 (induced by source paper "panda facilitating usable ai development"):
  with members:
    - "rafiki machine learning as an analytics service system"
    - "medical concept embedding with time aware attention"
    - "forkbase an efficient storage engine for blockchain and forkable applications"
    - "progressive neural architecture search"
    - "ease ml towards multi tenant resource sharing for machine learning workloads"
    - "the case for learned index structures"
    - "thoracic disease identification and localization with limited supervision"
    - "scaling sgd batch size to 32k for imagenet training"

Connected through hyperedge HE-7 (induced by source paper "learning multiple levels of representations with kernel machines"):
  with members:
    - "layer wise training of deep networks using kernel similarity"
    - "deep forest towards an alternative to deep neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "decoupled neural interfaces using synthetic gradients"
    - "learning functions when is deep better than shallow"
    - "a neural algorithm of artistic style"
    - "deep kernel learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "rmdl random multimodel deep learning for classification"):
  with members:
    - "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots"
    - "gakco a fast gapped k mer string kernel using counting"
    - "deep forest towards an alternative to deep neural networks"
    - "hdltex hierarchical deep learning for text classification"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "pcanet a simple deep learning baseline for image classification"
    - "adam a method for stochastic optimization"

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)