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: "popular ensemble methods an empirical study"
  Abstract: An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund & Schapire, 1996; Schapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier - especially when using neural networks. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being examined. In fact, further results show that Boosting ensembles may overfit noisy data sets, thus decreasing its performance. Finally, consistent with previous studies, our work suggests that most of the gain in an ensemble's performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees.

Connected through hyperedge HE-1 (induced by source paper "batchensemble an alternative approach to efficient ensemble and lifelong learning"):
  with members:
    - "analyzing the role of model uncertainty for electronic health records"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "evaluating scalable bayesian deep learning methods for robust computer vision"
    - "experience replay for continual learning"
    - "efficient lifelong learning with a gem"
    - "reinforced continual learning"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-2 (induced by source paper "trained model fusion for object detection using gating network"):
  with members:
    - "adaptive weighting multi field of view cnn for semantic segmentation in pathology"
    - "yolov3 an incremental improvement"
    - "visual saliency prediction using a mixture of deep neural networks"
    - "partial adversarial domain adaptation"
    - "edf ensemble distill and fuse for easy video labeling"
    - "robust deep multi modal learning based on gated information fusion network"
    - "rank of experts detection network ensemble"
    - "focal loss for dense object detection"

Connected through hyperedge HE-3 (induced by source paper "watchdog real time vehicle tracking on geo distributed edge nodes"):
  with members:
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "learning discriminative features with multiple granularities for person re identification"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "learning to multitask"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "focal loss for dense object detection"
    - "squeeze and excitation networks"

Connected through hyperedge HE-4 (induced by source paper "towards crisp ml q a machine learning process model with quality assurance methodology"):
  with members:
    - "quantifying interpretability and trust in machine learning systems"
    - "machine learning testing survey landscapes and horizons"
    - "deep visual domain adaptation a survey"
    - "deep reinforcement learning"
    - "on the state of the art of evaluation in neural language models"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "machine teaching a new paradigm for building machine learning systems"
    - "deeptest automated testing of deep neural network driven autonomous cars"

Connected through hyperedge HE-5 (induced by source paper "a survey on distributed machine learning"):
  with members:
    - "mxnet mpi embedding mpi parallelism in parameter server task model for scaling deep learning"
    - "how to backdoor federated learning"
    - "no peek a survey of private distributed deep learning"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "scaling deep learning on gpu and knights landing clusters"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-6 (induced by source paper "feature mask network for person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "person re identification by camera correlation aware feature augmentation"
    - "harmonious attention network for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "re ranking person re identification with k reciprocal encoding"
    - "glad global local alignment descriptor for pedestrian retrieval"

Connected through hyperedge HE-7 (induced by source paper "ensemblenet end to end optimization of multi headed models"):
  with members:
    - "nextvlad an efficient neural network to aggregate frame level features for large scale video classification"
    - "knowledge distillation by on the fly native ensemble"
    - "deep mixture of experts via shallow embedding"
    - "large scale distributed neural network training through online distillation"
    - "tafe net task aware feature embeddings for efficient learning and inference"
    - "slowfast networks for video recognition"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deep mutual learning"

Connected through hyperedge HE-8 (induced by source paper "3d object recognition with ensemble learning a study of point cloud based deep learning models"):
  with members:
    - "a deeper look at 3d shape classifiers"
    - "learning 3d shapes as multi layered height maps using 2d convolutional networks"
    - "deep sets"
    - "so net self organizing network for point cloud analysis"
    - "dynamic graph cnn for learning on point clouds"
    - "scannet richly annotated 3d reconstructions of indoor scenes"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "mining point cloud local structures by kernel correlation and graph pooling"

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.AI(Artificial Intelligence)