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: "exploring the use of text classification in the legal domain"
  Abstract: In this paper, we investigate the application of text classification methods to support law professionals. We present several experiments applying machine learning techniques to predict with high accuracy the ruling of the French Supreme Court and the law area to which a case belongs to. We also investigate the influence of the time period in which a ruling was made on the form of the case description and the extent to which we need to mask information in a full case ruling to automatically obtain training and test data that resembles case descriptions. We developed a mean probability ensemble system combining the output of multiple SVM classifiers. We report results of 98% average F1 score in predicting a case ruling, 96% F1 score for predicting the law area of a case, and 87.07% F1 score on estimating the date of a ruling.

Connected through hyperedge HE-1 (induced by source paper "an external knowledge enhanced multi label charge prediction approach with label number learning"):
  with members:
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "self attention with relative position representations"
    - "cail2018 a large scale legal dataset for judgment prediction"
    - "empirical study of deep learning for text classification in legal document review"
    - "sgm sequence generation model for multi label classification"
    - "attnconvnet at semeval 2018 task 1 attention based convolutional neural networks for multi label emotion classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "creating auxiliary representations from charge definitions for criminal charge prediction"):
  with members:
    - "interpretable charge predictions for criminal cases learning to generate court views from fact descriptions"
    - "cail2018 a large scale legal dataset for judgment prediction"
    - "collaborative memory network for recommendation systems"
    - "attention is all you need"
    - "learning to predict charges for criminal cases with legal basis"
    - "dynamic memory networks for visual and textual question answering"
    - "ask me anything dynamic memory networks for natural language processing"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "legal judgment prediction via multi perspective bi feedback network"):
  with members:
    - "interpretable charge predictions for criminal cases learning to generate court views from fact descriptions"
    - "cail2018 a large scale legal dataset for judgment prediction"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "neural relation extraction via inner sentence noise reduction and transfer learning"
    - "learning to predict charges for criminal cases with legal basis"
    - "a survey on multi task learning"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "multi task sequence to sequence learning"

Connected through hyperedge HE-4 (induced by source paper "experiments in cuneiform language identification"):
  with members:
    - "moroco the moldavian and romanian dialectal corpus"
    - "language and dialect identification of cuneiform texts"
    - "automatic language identification in texts a survey"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "finding function in form compositional character models for open vocabulary word representation"

Connected through hyperedge HE-5 (induced by source paper "automatic judgment prediction via legal reading comprehension"):
  with members:
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention over attention neural networks for reading comprehension"
    - "learning to predict charges for criminal cases with legal basis"
    - "dureader a chinese machine reading comprehension dataset from real world applications"
    - "learning to match using local and distributed representations of text for web search"
    - "squad 100 000 questions for machine comprehension of text"
    - "ms marco a human generated machine reading comprehension dataset"
    - "gated attention readers for text comprehension"

Connected through hyperedge HE-6 (induced by source paper "multi task deep learning for legal document translation summarization and multi label classification"):
  with members:
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "attention is all you need"
    - "generative adversarial network for abstractive text summarization"
    - "a generalized recurrent neural architecture for text classification with multi task learning"
    - "an overview of multi task learning in deep neural networks"
    - "one model to learn them all"
    - "recurrent neural network for text classification with multi task learning"

Connected through hyperedge HE-7 (induced by source paper "classifying patent applications with ensemble methods"):
  with members:
    - "challenges in discriminating profanity from hate speech"
    - "overview of cail2018 legal judgment prediction competition"

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)