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: "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
  Abstract: We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-the-art pre-trained models, training scripts, and training logs, to facilitate rapid prototyping and promote reproducible research. We also provide modular APIs with flexible building blocks to enable efficient customization. Leveraging the MXNet ecosystem, the deep learning models in GluonCV and GluonNLP can be deployed onto a variety of platforms with different programming languages. Benefiting from open source under the Apache 2.0 license, GluonCV and GluonNLP have attracted 100 contributors worldwide on GitHub. Models of GluonCV and GluonNLP have been downloaded for more than 1.6 million times in fewer than 10 months.

Connected through hyperedge HE-1 (induced by source paper "attention guided chained context aggregation for semantic segmentation"):
  with members:
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "selective kernel networks"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "deep high resolution representation learning for human pose estimation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "ccnet criss cross attention for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "leep a new measure to evaluate transferability of learned representations"):
  with members:
    - "dataset2vec learning dataset meta features"
    - "a baseline for few shot image classification"
    - "which tasks should be learned together in multi task learning"
    - "p2l predicting transfer learning for images and semantic relations"
    - "transferability and hardness of supervised classification tasks"
    - "task2vec task embedding for meta learning"
    - "fixynn efficient hardware for mobile computer vision via transfer learning"
    - "meta dataset a dataset of datasets for learning to learn from few examples"

Connected through hyperedge HE-3 (induced by source paper "meliusnet can binary neural networks achieve mobilenet level accuracy"):
  with members:
    - "bayesian optimized 1 bit cnns"
    - "on the variance of the adaptive learning rate and beyond"
    - "searching for accurate binary neural architectures"
    - "projection convolutional neural networks for 1 bit cnns via discrete back propagation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "training competitive binary neural networks from scratch"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-4 (induced by source paper "cotk an open source toolkit for fast development and fair evaluation of text generation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "challenges in building intelligent open domain dialog systems"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "allennlp a deep semantic natural language processing platform"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "texygen a benchmarking platform for text generation models"
    - "emotional chatting machine emotional conversation generation with internal and external memory"

Connected through hyperedge HE-5 (induced by source paper "laprop a better way to combine momentum with adaptive gradient"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "optimization for deep learning theory and algorithms"
    - "on the variance of the adaptive learning rate and beyond"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "roberta a robustly optimized bert pretraining approach"
    - "gradient descent finds global minima of deep neural networks"
    - "a general system of differential equations to model first order adaptive algorithms"

Connected through hyperedge HE-6 (induced by source paper "face detection with feature pyramids and landmarks"):
  with members:
    - "robust and high performance face detector"
    - "self driving cars a survey"
    - "refineface refinement neural network for high performance face detection"
    - "searching for mobilenetv3"
    - "pyramidbox high performance detector for finding tiny face"
    - "extd extremely tiny face detector via iterative filter reuse"
    - "retinaface single stage dense face localisation in the wild"
    - "accurate face detection for high performance"

Connected through hyperedge HE-7 (induced by source paper "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"):
  with members:
    - "hetconv heterogeneous kernel based convolutions for deep cnns"
    - "deep high resolution representation learning for human pose estimation"
    - "elastic improving cnns with dynamic scaling policies"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "multi fiber networks for video recognition"
    - "regularized evolution for image classifier architecture search"
    - "a short note about kinetics 600"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-8 (induced by source paper "hybrid composition with idleblock more efficient networks for image recognition"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "searching for mobilenetv3"
    - "deep high resolution representation learning for human pose estimation"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "dynamic mini batch sgd for elastic distributed training learning in the limbo of resources"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"

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)