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: "deep bayesian active learning for natural language processing results of a large scale empirical study"
  Abstract: Several recent papers investigate Active Learning (AL) for mitigating the data dependence of deep learning for natural language processing. However, the applicability of AL to real-world problems remains an open question. While in supervised learning, practitioners can try many different methods, evaluating each against a validation set before selecting a model, AL affords no such luxury. Over the course of one AL run, an agent annotates its dataset exhausting its labeling budget. Thus, given a new task, an active learner has no opportunity to compare models and acquisition functions. This paper provides a large scale empirical study of deep active learning, addressing multiple tasks and, for each, multiple datasets, multiple models, and a full suite of acquisition functions. We find that across all settings, Bayesian active learning by disagreement, using uncertainty estimates provided either by Dropout or Bayes-by Backprop significantly improves over i.i.d. baselines and usually outperforms classic uncertainty sampling.

Connected through hyperedge HE-1 (induced by source paper "domain independent extraction of scientific concepts from research articles"):
  with members:
    - "structural scaffolds for citation intent classification in scientific publications"
    - "investigating correlations of inter coder agreement and machine annotation performance for historical video data"
    - "hierarchical neural networks for sequential sentence classification in medical scientific abstracts"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "allennlp a deep semantic natural language processing platform"
    - "construction of the literature graph in semantic scholar"
    - "end to end neural coreference resolution"
    - "deep active learning for named entity recognition"

Connected through hyperedge HE-2 (induced by source paper "deep active learning for efficient training of a lidar 3d object detector"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "cereals cost effective region based active learning for semantic segmentation"
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "large scale visual active learning with deep probabilistic ensembles"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "localization aware active learning for object detection"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-3 (induced by source paper "model based interactive semantic parsing a unified framework and a text to sql case study"):
  with members:
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "learning to learn semantic parsers from natural language supervision"
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "representing schema structure with graph neural networks for text to sql parsing"
    - "quantifying uncertainties in natural language processing tasks"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sql to text generation with graph to sequence model"
    - "sqlnet generating structured queries from natural language without reinforcement learning"

Connected through hyperedge HE-4 (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"
    - "practical obstacles to deploying active learning"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "putting question answering systems into practice transfer learning for efficient domain customization"):
  with members:
    - "deep learning in business analytics and operations research models applications and managerial implications"
    - "deep learning for affective computing text based emotion recognition in decision support"
    - "adaptive document retrieval for deep question answering"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "reading wikipedia to answer open domain questions"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-6 (induced by source paper "interactive text ranking with bayesian optimisation a case study on community qa and summarisation"):
  with members:
    - "end to end robotic reinforcement learning without reward engineering"
    - "passage ranking with weak supervision"
    - "preference based interactive multi document summarisation"
    - "april interactively learning to summarise by combining active preference learning and reinforcement learning"
    - "active learning for interactive neural machine translation of data streams"
    - "finding convincing arguments using scalable bayesian preference learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improving a neural semantic parser by counterfactual learning from human bandit feedback"

Connected through hyperedge HE-7 (induced by source paper "batchbald efficient and diverse batch acquisition for deep bayesian active learning"):
  with members:
    - "variational adversarial active learning"
    - "cinic 10 is not imagenet or cifar 10"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "deep active learning for named entity recognition"
    - "cost effective active learning for deep image classification"
    - "deep bayesian active learning with image data"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "investigating the effectiveness of word embedding based active learning for labelling text datasets"):
  with members:
    - "improving active learning in systematic reviews"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learned in translation contextualized word vectors"
    - "fasttext zip compressing text classification models"
    - "stability of topic modeling via matrix factorization"
    - "enriching word vectors with subword information"

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.CL(Computation and Language)