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: "cail2018 a large scale legal dataset for judgment prediction"
  Abstract: In this paper, we introduce the \textbf{C}hinese \textbf{AI} and \textbf{L}aw challenge dataset (CAIL2018), the first large-scale Chinese legal dataset for judgment prediction. \dataset contains more than $2.6$ million criminal cases published by the Supreme People's Court of China, which are several times larger than other datasets in existing works on judgment prediction. Moreover, the annotations of judgment results are more detailed and rich. It consists of applicable law articles, charges, and prison terms, which are expected to be inferred according to the fact descriptions of cases. For comparison, we implement several conventional text classification baselines for judgment prediction and experimental results show that it is still a challenge for current models to predict the judgment results of legal cases, especially on prison terms. To help the researchers make improvements on legal judgment prediction, both \dataset and baselines will be released after the CAIL competition\footnote{this http URL}.

Connected through hyperedge HE-1 (induced by source paper "jec qa a legal domain question answering dataset"):
  with members:
    - "convolutional spatial attention model for reading comprehension with multiple choice questions"
    - "interpretable charge predictions for criminal cases learning to generate court views from fact descriptions"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "know what you don t know unanswerable questions for squad"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "secaps a sequence enhanced capsule model for charge prediction"

Connected through hyperedge HE-2 (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"
    - "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"
    - "where and when to look deep siamese attention networks for video based person re identification"

Connected through hyperedge HE-3 (induced by source paper "cjrc a reliable human annotated benchmark dataset for chinese judicial reading comprehension"):
  with members:
    - "overview of cail2018 legal judgment prediction competition"
    - "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-4 (induced by source paper "geosqa a benchmark for scenario based question answering in the geography domain at high school level"):
  with members:
    - "interpretable charge predictions for criminal cases learning to generate court views from fact descriptions"
    - "xnli evaluating cross lingual sentence representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "natural language inference over interaction space"
    - "race large scale reading comprehension dataset from examinations"
    - "learning to predict charges for criminal cases with legal basis"
    - "dureader a chinese machine reading comprehension dataset from real world applications"
    - "bilateral multi perspective matching for natural language sentences"

Connected through hyperedge HE-5 (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"
    - "overview of cail2018 legal judgment prediction competition"
    - "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-6 (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"
    - "collaborative memory network for recommendation systems"
    - "attention is all you need"
    - "learning to predict charges for criminal cases with legal basis"
    - "exploring the use of text classification in the legal domain"
    - "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-7 (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"
    - "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"
    - "exploring the use of text classification in the legal domain"
    - "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-8 (induced by source paper "charge based prison term prediction with deep gating network"):
  with members:
    - "transformation networks for target oriented sentiment classification"
    - "overview of cail2018 legal judgment prediction competition"
    - "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"

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)