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: "tensorlayer a versatile library for efficient deep learning development"
  Abstract: Recently we have observed emerging uses of deep learning techniques in multimedia systems. Developing a practical deep learning system is arduous and complex. It involves labor-intensive tasks for constructing sophisticated neural networks, coordinating multiple network models, and managing a large amount of training-related data. To facilitate such a development process, we propose TensorLayer which is a Python-based versatile deep learning library. TensorLayer provides high-level modules that abstract sophisticated operations towards neuron layers, network models, training data and dependent training jobs. In spite of offering simplicity, it has transparent module interfaces that allows developers to flexibly embed low-level controls within a backend engine, with the aim of supporting fine-grain tuning towards training. Real-world cluster experiment results show that TensorLayeris able to achieve competitive performance and scalability in critical deep learning tasks. TensorLayer was released in September 2016 on GitHub. Since after, it soon become one of the most popular open-sourced deep learning library used by researchers and practitioners.

Connected through hyperedge HE-1 (induced by source paper "fast video object segmentation with spatio temporal gans"):
  with members:
    - "video to video synthesis"
    - "learning to adapt structured output space for semantic segmentation"
    - "unsupervised person image synthesis in arbitrary poses"
    - "fast and accurate online video object segmentation via tracking parts"
    - "deep spatio temporal random fields for efficient video segmentation"
    - "efficient video object segmentation via network modulation"
    - "maskrnn instance level video object segmentation"
    - "videomatch matching based video object segmentation"

Connected through hyperedge HE-2 (induced by source paper "training free uncertainty estimation for neural networks"):
  with members:
    - "sampling free epistemic uncertainty estimation using approximated variance propagation"
    - "reducing uncertainty in undersampled mri reconstruction with active acquisition"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "deep bayesian active learning with image data"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deeper depth prediction with fully convolutional residual networks"
    - "loss aware binarization of deep networks"
    - "natural parameter networks a class of probabilistic neural networks"

Connected through hyperedge HE-3 (induced by source paper "cloudlstm a recurrent neural model for spatiotemporal point cloud stream forecasting"):
  with members:
    - "multi service mobile traffic forecasting via convolutional long short term memories"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "machine learning for spatiotemporal sequence forecasting a survey"
    - "deep learning in mobile and wireless networking a survey"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "zipnet gan inferring fine grained mobile traffic patterns via a generative adversarial neural network"
    - "deformable convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "a modular benchmarking infrastructure for high performance and reproducible deep learning"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "sparcml high performance sparse communication for machine learning"
    - "tvm end to end optimization stack for deep learning"
    - "regularized evolution for image classifier architecture search"
    - "online adaptive methods universality and acceleration"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-5 (induced by source paper "integrating semantic knowledge to tackle zero shot text classification"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "a large scale attribute dataset for zero shot learning"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "data augmentation for low resource neural machine translation"
    - "train once test anywhere zero shot learning for text classification"
    - "data noising as smoothing in neural network language models"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"

Connected through hyperedge HE-6 (induced by source paper "a robust iris authentication system on gpu based edge devices using multi modalities learning model"):
  with members:
    - "deep learning on mobile devices a review"
    - "on the convergence of adam and beyond"
    - "stacked pooling improving crowd counting by boosting scale invariance"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "decoupled weight decay regularization"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "an overview of gradient descent optimization algorithms"
    - "cyclical learning rates for training neural networks"

Connected through hyperedge HE-7 (induced by source paper "multi service mobile traffic forecasting via convolutional long short term memories"):
  with members:
    - "deep learning in mobile and wireless networking a survey"
    - "long term mobile traffic forecasting using deep spatio temporal neural networks"
    - "tensorflow a system for large scale machine learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-8 (induced by source paper "niftynet a deep learning platform for medical imaging"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks"
    - "freehand ultrasound image simulation with spatially conditioned generative adversarial networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "a self aware sampling scheme to efficiently train fully convolutional networks for semantic segmentation"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "a survey on deep learning in medical image analysis"

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)