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: "empirical evaluations of active learning strategies in legal document review"
  Abstract: One type of machine learning, text classification, is now regularly applied in the legal matters involving voluminous document populations because it can reduce the time and expense associated with the review of those documents. One form of machine learning — Active Learning — has drawn attention from the legal community because it offers the potential to make the machine learning process even more effective. Active Learning, applied to legal documents, is considered a new technology in the legal domain and is continuously applied to all documents in a legal matter until an insignificant number of relevant documents are left for review. This implementation is slightly different than traditional implementations of Active Learning where the process stops once achieving acceptable model performance. The purpose of this paper is twofold: (i) to question whether Active Learning actually is a superior learning methodology and (ii) to highlight the ways that Active Learning can be most effectively applied to real legal industry data. Unlike other studies, our experiments were performed against large data sets taken from recent, real-world legal matters covering a variety of areas. We conclude that, although these experiments show the Active Learning strategy popularly used in legal document review can quickly identify informative training documents, it becomes less effective over time. In particular, our findings suggest this most popular form of Active Learning in the legal arena, where the highest-scoring documents are selected as training examples, is in fact not the most efficient approach in most instances. Ultimately, a different Active Learning strategy may be best suited to initiate the predictive modeling process but not to continue through the entire document review.

Connected through hyperedge HE-1 (induced by source paper "predicting the amount of gdpr fines"):
  with members:
    - "the general data protection regulation requirements architectures and constraints"
    - "toward validation of textual information retrieval techniques for software weaknesses"

Connected through hyperedge HE-2 (induced by source paper "a framework for explainable text classification in legal document review"):
  with members:
    - "understanding convolutional neural networks for text classification"
    - "towards explainable nlp a generative explanation framework for text classification"
    - "explainable text classification in legal document review a case study of explainable predictive coding"
    - "towards interpretable deep neural networks by leveraging adversarial examples"
    - "empirical evaluations of preprocessing parameters impact on predictive coding s effectiveness"
    - "rationalizing neural predictions"
    - "why should i trust you explaining the predictions of any classifier"
    - "rationale augmented convolutional neural networks for text classification"

Connected through hyperedge HE-3 (induced by source paper "evaluation of seed set selection approaches and active learning strategies in predictive coding"):
  with members:
    - "empirical evaluations of preprocessing parameters impact on predictive coding s effectiveness"
    - "autonomy and reliability of continuous active learning for technology assisted review"

Connected through hyperedge HE-4 (induced by source paper "explainable text classification in legal document review a case study of explainable predictive coding"):
  with members:
    - "towards interpretable deep neural networks by leveraging adversarial examples"
    - "empirical evaluations of preprocessing parameters impact on predictive coding s effectiveness"
    - "rationalizing neural predictions"
    - "rationale augmented convolutional neural networks for text classification"

Connected through hyperedge HE-5 (induced by source paper "empirical study of deep learning for text classification in legal document review"):
  with members:
    - "explainable text classification in legal document review a case study of explainable predictive coding"
    - "empirical evaluations of preprocessing parameters impact on predictive coding s effectiveness"
    - "a primer on neural network models for natural language processing"

Connected through hyperedge HE-6 (induced by source paper "an empirical study of the application of machine learning and keyword terms methodologies to privilege document review projects in legal matters"):
  with members:
    - "empirical evaluations of preprocessing parameters impact on predictive coding s effectiveness"

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.IR(Information Retrieval)