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: "tensorflow large scale machine learning on heterogeneous distributed systems"
  Abstract: TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. The system is flexible and can be used to express a wide variety of algorithms, including training and inference algorithms for deep neural network models, and it has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields, including speech recognition, computer vision, robotics, information retrieval, natural language processing, geographic information extraction, and computational drug discovery. This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google. The TensorFlow API and a reference implementation were released as an open-source package under the Apache 2.0 license in November, 2015 and are available at www.tensorflow.org.

Connected through hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis"):
  with members:
    - "session based recommendation with graph neural network"
    - "contextual hybrid session based news recommendation with recurrent neural networks"
    - "on the importance of news content representation in hybrid neural session based recommender systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "neurec on nonlinear transformation for personalized ranking"
    - "sequence aware recommender systems"
    - "evaluation of session based recommendation algorithms"
    - "multi pointer co attention networks for recommendation"

Connected through hyperedge HE-2 (induced by source paper "hyper parameter optimization a review of algorithms and applications"):
  with members:
    - "a generalized framework for population based training"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "meta learning a survey"
    - "deep learning using rectified linear units relu"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "massively parallel hyperparameter tuning"
    - "combination of hyperband and bayesian optimization for hyperparameter optimization in deep learning"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-3 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "on the dimensionality of word embedding"
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning transferable architectures for scalable image recognition"
    - "attention is all you need"
    - "multilingual hierarchical attention networks for document classification"
    - "a read write memory network for movie story understanding"

Connected through hyperedge HE-4 (induced by source paper "automatic perturbation analysis on general computational graphs"):
  with members:
    - "robustness verification for transformers"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "fastened crown tightened neural network robustness certificates"
    - "certified robustness to adversarial word substitutions"
    - "verifying aircraft collision avoidance neural networks through linear approximations of safe regions"
    - "achieving verified robustness to symbol substitutions via interval bound propagation"
    - "popqorn quantifying robustness of recurrent neural networks"
    - "asymmetric valleys beyond sharp and flat local minima"

Connected through hyperedge HE-5 (induced by source paper "object adaptive lstm network for real time visual tracking with adversarial data augmentation"):
  with members:
    - "quadruplet network with one shot learning for fast visual object tracking"
    - "deep visual attention prediction"
    - "context aware deep feature compression for high speed visual tracking"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "video salient object detection via fully convolutional networks"
    - "vital visual tracking via adversarial learning"
    - "learning dynamic memory networks for object tracking"

Connected through hyperedge HE-6 (induced by source paper "sideways depth parallel training of video models"):
  with members:
    - "backprop diffusion is biologically plausible"
    - "decoupled greedy learning of cnns"
    - "dynamonet dynamic action and motion network"
    - "putting an end to end to end gradient isolated learning of representations"
    - "scsampler sampling salient clips from video for efficient action recognition"
    - "rethinking imagenet pre training"
    - "learning visual question answering by bootstrapping hard attention"
    - "massively parallel video networks"

Connected through hyperedge HE-7 (induced by source paper "compounding the performance improvements of assembled techniques in a convolutional neural network"):
  with members:
    - "selective kernel networks"
    - "adversarial examples improve image recognition"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "do better imagenet models transfer better"
    - "bag of tricks for image classification with convolutional neural networks"
    - "batch dropblock network for person re identification and beyond"
    - "dropblock a regularization method for convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "an ensemble of epoch wise empirical bayes for few shot learning"):
  with members:
    - "multi level semantic feature augmentation for one shot learning"
    - "meta learning with differentiable convex optimization"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "lgm net learning to generate matching networks for few shot learning"
    - "finding task relevant features for few shot learning by category traversal"
    - "meta transfer learning through hard tasks"
    - "a closer look at few shot classification"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"

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.DC(Distributed, Parallel, and Cluster Computing)