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: "predicting the law area and decisions of french supreme court cases"
  Abstract: In this paper, we investigate the application of text classification methods to predict the law area and the decision of cases judged by the French Supreme Court. We also investigate the influence of the time period in which a ruling was made over the textual form of the case description and the extent to which it is necessary to mask the judge's motivation for a ruling to emulate a real-world test scenario. We report results of 96% f1 score in predicting a case ruling, 90% f1 score in predicting the law area of a case, and 75.9% f1 score in estimating the time span when a ruling has been issued using a linear Support Vector Machine (SVM) classifier trained on lexical features.

Connected through hyperedge HE-1 (induced by source paper "neural legal judgment prediction in english"):
  with members:
    - "extreme multi label legal text classification a case study in eu legislation"
    - "attention is not explanation"
    - "interpretable charge predictions for criminal cases learning to generate court views from fact descriptions"
    - "explainable prediction of medical codes from clinical text"
    - "obligation and prohibition extraction using hierarchical rnns"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "legal area classification a comparative study of text classifiers on singapore supreme court judgments"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"
    - "do convolutional networks need to be deep for text classification"
    - "replace or retrieve keywords in documents at scale"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-3 (induced by source paper "exploring the use of text classification in the legal domain"):
  with members:
    - "antisocial behavior in online discussion communities"

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.CL(Computation and Language)