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 preprocessing parameters impact on predictive coding s effectiveness"
  Abstract: Predictive coding, once used in only a small fraction of legal and business matters, is now widely deployed to quickly cull through increasingly vast amounts of data and reduce the need for costly and inefficient human document review. Previously, the sole front-end input used to create a predictive model was the exemplar documents (training data) chosen by subject-matter experts. Many predictive coding tools require users to rely on static preprocessing parameters and a single machine learning algorithm to develop the predictive model. Little research has been published discussing the impact preprocessing parameters and learning algorithms have on the effectiveness of the technology. A deeper dive into the generation of a predictive model shows that the settings and algorithm can have a strong effect on the accuracy and efficacy of a predictive coding tool. Understanding how these input parameters affect the output will empower legal teams with the information they need to implement predictive coding as efficiently and effectively as possible. This paper outlines different preprocessing parameters and algorithms as applied to multiple real-world data sets to understand the influence of various approaches.

Connected through hyperedge HE-1 (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"
    - "empirical evaluations of active learning strategies in legal document review"
    - "towards interpretable deep neural networks by leveraging adversarial examples"
    - "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-2 (induced by source paper "image analytics for legal document review a transfer learning approach"):
  with members:
    - "empirical study of deep learning for text classification in legal document review"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"

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 active learning strategies in legal document review"
    - "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:
    - "empirical evaluations of active learning strategies in legal document review"
    - "towards interpretable deep neural networks by leveraging adversarial examples"
    - "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 active learning strategies in legal document review"
    - "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 active learning strategies in legal document review"

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)