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: "autoencoder by forest"
  Abstract: Auto-encoding is an important task which is typically realized by deep neural networks (DNNs) such as convolutional neural networks (CNN). In this paper, we propose EncoderForest (abbrv. eForest), the first tree ensemble based auto-encoder. We present a procedure for enabling forests to do backward reconstruction by utilizing the equivalent classes defined by decision paths of the trees, and demonstrate its usage in both supervised and unsupervised setting. Experiments show that, compared with DNN autoencoders, eForest is able to obtain lower reconstruction error with fast training speed, while the model itself is reusable and damage-tolerable.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "rectified decision trees towards interpretability compression and empirical soundness"):
  with members:
    - "interpreting deep visual representations via network dissection"
    - "deep forest towards an alternative to deep neural networks"
    - "dynamic routing between capsules"
    - "examining cnn representations with respect to dataset bias"
    - "distilling a neural network into a soft decision tree"
    - "channel pruning for accelerating very deep neural networks"
    - "xgboost a scalable tree boosting system"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"

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"
    - "deep forest towards an alternative to deep neural networks"
    - "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"
    - "deep forest towards an alternative to deep neural networks"
    - "learning deep resnet blocks sequentially using boosting theory"
    - "xgboost a scalable tree boosting system"
    - "on the doubt about margin explanation of boosting"

Connected through hyperedge HE-6 (induced by source paper "composite neural network theory and application to pm2 5 prediction"):
  with members:
    - "temporal relational ranking for stock prediction"
    - "theoretical investigation of composite neural network"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "issues in stacked generalization"

Connected through hyperedge HE-7 (induced by source paper "theoretical investigation of composite neural network"):
  with members:
    - "deep contextualized word representations"
    - "deep residual learning for image recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "imagenet large scale visual recognition challenge"
    - "popular ensemble methods an empirical study"
    - "issues in stacked generalization"

Connected through hyperedge HE-8 (induced by source paper "hdi forest highest density interval regression forest"):
  with members:
    - "multi layered gradient boosting decision trees"

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)