human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep active learning for named entity recognition"):
  Abstract: Deep learning has yielded state-of-the-art performance on many natural language processing tasks including named entity recognition (NER). However, this typically requires large amounts of labeled data. In this work, we demonstrate that the amount of labeled training data can be drastically reduced when deep learning is combined with active learning. While active learning is sample-efficient, it can be computationally expensive since it requires iterative retraining. To speed this up, we introduce a lightweight architecture for NER, viz., the CNN-CNN-LSTM model consisting of convolutional character and word encoders and a long short term memory (LSTM) tag decoder. The model achieves nearly state-of-the-art performance on standard datasets for the task while being computationally much more efficient than best performing models. We carry out incremental active learning, during the training process, and are able to nearly match state-of-the-art performance with just 25\% of the original training data.

Includes members:
  Member "neural models for sequence chunking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hybrid neural tagging model for open relation extraction")
  Member "fast and accurate entity recognition with iterated dilated convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dense dilated convolutions merging network for land cover classification")
  Member "reporting score distributions makes a difference performance study of lstm networks for sequence tagging":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards the systematic reporting of the energy and carbon footprints of machine learning")
  Member "what uncertainties do we need in bayesian deep learning for computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "deep bayesian active learning with image data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "action for better prediction")
  Member "revisiting perceptron efficient and label optimal learning of halfspaces":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "globally normalized transition based neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "consistency of a recurrent language model with respect to incomplete decoding")
  Member "toward mention detection robustness with recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "graph based neural networks for event factuality prediction using syntactic and semantic structures")

Question: Please tell me which class the source paper 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)