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: "practical obstacles to deploying active learning"
  Abstract: Active learning (AL) is a widely-used training strategy for maximizing predictive performance subject to a fixed annotation budget. In AL one iteratively selects training examples for annotation, often those for which the current model is most uncertain (by some measure). The hope is that active sampling leads to better performance than would be achieved under independent and identically distributed (i.i.d.) random samples. While AL has shown promise in retrospective evaluations, these studies often ignore practical obstacles to its use. In this paper we show that while AL may provide benefits when used with specific models and for particular domains, the benefits of current approaches do not generalize reliably across models and tasks. This is problematic because in practice one does not have the opportunity to explore and compare alternative AL strategies. Moreover, AL couples the training dataset with the model used to guide its acquisition. We find that subsequently training a successor model with an actively-acquired dataset does not consistently outperform training on i.i.d. sampled data. Our findings raise the question of whether the downsides inherent to AL are worth the modest and inconsistent performance gains it tends to afford.

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"
    - "how transferable are the datasets collected by active learners"
    - "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 "a survey on active learning and human in the loop deep learning for medical image analysis"):
  with members:
    - "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "continual lifelong learning with neural networks a review"
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "generative adversarial network in medical imaging a review"
    - "geometry in active learning for binary and multi class image segmentation"
    - "an overview of deep learning in medical imaging focusing on mri"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"

Connected through hyperedge HE-3 (induced by source paper "sampling bias in deep active classification an empirical study"):
  with members:
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "are all training examples created equal an empirical study"
    - "adversarial active learning for deep networks a margin based approach"
    - "discriminative active learning"
    - "an empirical study of example forgetting during deep neural network learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "when will gradient methods converge to max margin classifier under relu models"

Connected through hyperedge HE-4 (induced by source paper "less is more an exploration of data redundancy with active dataset subsampling"):
  with members:
    - "billion scale semi supervised learning for image classification"
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "are all training examples created equal an empirical study"
    - "adaptive semantic segmentation with a strategic curriculum of proxy labels"
    - "an empirical study of example forgetting during deep neural network learning"
    - "large scale visual active learning with deep probabilistic ensembles"
    - "deep ensemble bayesian active learning addressing the mode collapse issue in monte carlo dropout via ensembles"
    - "revisiting unreasonable effectiveness of data in deep learning era"

Connected through hyperedge HE-5 (induced by source paper "overcoming practical issues of deep active learning and its applications on named entity recognition"):
  with members:
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "learning how to active learn a deep reinforcement learning approach"
    - "learning active learning from data"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "learning algorithms for active learning"
    - "deep learning scaling is predictable empirically"
    - "a method for stopping active learning based on stabilizing predictions and the need for user adjustable stopping"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "on the limits of learning to actively learn semantic representations"):
  with members:
    - "allennlp a deep semantic natural language processing platform"
    - "multitask parsing across semantic representations"
    - "large scale qa srl parsing"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "learning how to active learn a deep reinforcement learning approach"
    - "learning active learning from data"
    - "learning algorithms for active learning"

Connected through hyperedge HE-7 (induced by source paper "understanding goal oriented active learning via influence functions"):
  with members:
    - "on the accuracy of influence functions for measuring group effects"
    - "on the relationship between data efficiency and error for uncertainty sampling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "convergence rates of active learning for maximum likelihood estimation"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

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)