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: "explainable text classification in legal document review a case study of explainable predictive coding"
  Abstract: In today’s legal environment, lawsuits and regulatory investigations require companies to embark upon increasingly intensive data-focused engagements to identify, collect and analyze large quantities of data. When documents are staged for review – where they are typically assessed for relevancy or privilege – the process can require companies to dedicate an extraordinary level of resources, both with respect to human resources, but also with respect to the use of technology-based techniques to intelligently sift through data. Companies regularly spend millions of dollars producing ‘responsive’ electronically-stored documents for these types of matters. For several years, attorneys have been using a variety of tools to conduct this exercise, and most recently, they are accepting the use of machine learning techniques like text classification (referred to as predictive coding in the legal industry) to efficiently cull massive volumes of data to identify responsive documents for use in these matters. In recent years, a group of AI and Machine Learning researchers have been actively researching Explainable AI. In an explainable AI system, actions or decisions are human understandable. In typical legal ‘document review’ scenarios, a document can be identified as responsive, as long as one or more of the text snippets (small passages of text) in a document are deemed responsive. In these scenarios, if predictive coding can be used to locate these responsive snippets, then attorneys could easily evaluate the model’s document classification decision. When deployed with defined and explainable results, predictive coding can drastically enhance the overall quality and speed of the document review process by reducing the time it takes to review documents. Moreover, explainable predictive coding provides lawyers with greater confidence in the results of that supervised learning task. The authors of this paper propose the concept of explainable predictive coding and simple explainable predictive coding methods to locate responsive snippets within responsive documents. We also report our preliminary experimental results using the data from an actual legal matter that entailed this type of document review. The purpose of this paper is to demonstrate the feasibility of explainable predictive coding in the context of professional services in the legal space.

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"
    - "empirical evaluations of active learning strategies in legal document review"
    - "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-2 (induced by source paper "empirical study of deep learning for text classification in legal document review"):
  with members:
    - "empirical evaluations of active learning strategies in legal document review"
    - "empirical evaluations of preprocessing parameters impact on predictive coding s effectiveness"
    - "a primer on neural network models for natural language processing"

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)