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: "overview of cail2018 legal judgment prediction competition"
  Abstract: In this paper, we give an overview of the Legal Judgment Prediction (LJP) competition at Chinese AI and Law challenge (CAIL2018). This competition focuses on LJP which aims to predict the judgment results according to the given facts. Specifically, in CAIL2018 , we proposed three subtasks of LJP for the contestants, i.e., predicting relevant law articles, charges and prison terms given the fact descriptions. CAIL2018 has attracted several hundreds participants (601 teams, 1, 144 contestants from 269 organizations). In this paper, we provide a detailed overview of the task definition, related works, outstanding methods and competition results in CAIL2018.

Connected through hyperedge HE-1 (induced by source paper "cjrc a reliable human annotated benchmark dataset for chinese judicial reading comprehension"):
  with members:
    - "cail2018 a large scale legal dataset for judgment prediction"
    - "know what you don t know unanswerable questions for squad"
    - "neural entity reasoner for global consistency in ner"
    - "coqa a conversational question answering challenge"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "race large scale reading comprehension dataset from examinations"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention over attention neural networks for reading comprehension"

Connected through hyperedge HE-2 (induced by source paper "cail2019 scm a dataset of similar case matching in legal domain"):
  with members:
    - "neural legal judgment prediction in english"
    - "interpretable charge predictions for criminal cases learning to generate court views from fact descriptions"
    - "cail2018 a large scale legal dataset for judgment prediction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "matching natural language sentences with hierarchical sentence factorization"
    - "learning to predict charges for criminal cases with legal basis"
    - "event identification as a decision process with non linear representation of text"
    - "enhanced lstm for natural language inference"

Connected through hyperedge HE-3 (induced by source paper "charge based prison term prediction with deep gating network"):
  with members:
    - "transformation networks for target oriented sentiment classification"
    - "cail2018 a large scale legal dataset for judgment prediction"
    - "learning to predict charges for criminal cases with legal basis"
    - "aspect level sentiment classification with deep memory network"
    - "text matching as image recognition"
    - "convolutional neural network architectures for matching natural language sentences"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-4 (induced by source paper "classifying patent applications with ensemble methods"):
  with members:
    - "challenges in discriminating profanity from hate speech"
    - "exploring the use of text classification in the legal domain"

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.AI(Artificial Intelligence)