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: "how transferable are the datasets collected by active learners"
  Abstract: Active learning is a widely-used training strategy for maximizing predictive performance subject to a fixed annotation budget. Between rounds of training, an active learner iteratively selects examples for annotation, typically based on some measure of the model's uncertainty, coupling the acquired dataset with the underlying model. However, owing to the high cost of annotation and the rapid pace of model development, labeled datasets may remain valuable long after a particular model is surpassed by new technology. In this paper, we investigate the transferability of datasets collected with an acquisition model A to a distinct successor model S. We seek to characterize whether the benefits of active learning persist when A and S are different models. To this end, we consider two standard NLP tasks and associated datasets: text classification and sequence tagging. We find that training S on a dataset actively acquired with a (different) model A typically yields worse performance than when S is trained with "native" data (i.e., acquired actively using S), and often performs worse than training on i.i.d. sampled data. These findings have implications for the use of active learning in practice,suggesting that it is better suited to cases where models are updated no more frequently than labeled data.

Connected through hyperedge HE-1 (induced by source paper "mots multi object tracking and segmentation"):
  with members:
    - "panoptic segmentation"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "practical obstacles to deploying active learning"
    - "combined image and world space tracking in traffic scenes"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"

Connected through hyperedge HE-2 (induced by source paper "selection via proxy efficient data selection for deep learning"):
  with members:
    - "batchbald efficient and diverse batch acquisition for deep bayesian active learning"
    - "weight agnostic neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "on the relationship between data efficiency and error for uncertainty sampling"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "active learning for interactive neural machine translation of data streams"
    - "an empirical study of example forgetting during deep neural network learning"
    - "revisiting unreasonable effectiveness of data in deep learning era"

Connected through hyperedge HE-3 (induced by source paper "training data distribution search with ensemble active learning"):
  with members:
    - "searching for a robust neural architecture in four gpu hours"
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "variational adversarial active learning"
    - "automl a survey of the state of the art"
    - "are all training examples created equal an empirical study"
    - "amc automl for model compression and acceleration on mobile devices"
    - "adaptive semantic segmentation with a strategic curriculum of proxy labels"
    - "an empirical study of example forgetting during deep neural network learning"

Connected through hyperedge HE-4 (induced by source paper "dynamic mode decomposition based feature for image classification"):
  with members:
    - "autoaugment learning augmentation policies from data"
    - "universal language model fine tuning for text classification"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "multi task learning of keyphrase boundary classification"
    - "snorkel rapid training data creation with weak supervision"
    - "learning from simulated and unsupervised images through adversarial training"
    - "active learning with statistical models"

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)