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: "learning to predict charges for criminal cases with legal basis"
  Abstract: The charge prediction task is to determine appropriate charges for a given case, which is helpful for legal assistant systems where the user input is fact description. We argue that relevant law articles play an important role in this task, and therefore propose an attention-based neural network method to jointly model the charge prediction task and the relevant article extraction task in a unified framework. The experimental results show that, besides providing legal basis, the relevant articles can also clearly improve the charge prediction results, and our full model can effectively predict appropriate charges for cases with different expression styles.

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"
    - "cail2018 a large scale legal dataset for judgment prediction"
    - "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"

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"
    - "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-3 (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"
    - "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"

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"
    - "cail2018 a large scale legal dataset for judgment prediction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "natural language inference over interaction space"
    - "race large scale reading comprehension dataset from examinations"
    - "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"
    - "cail2018 a large scale legal dataset for judgment prediction"
    - "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"
    - "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"
    - "cail2018 a large scale legal dataset for judgment prediction"
    - "collaborative memory network for recommendation systems"
    - "attention is all you need"
    - "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 "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-8 (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"
    - "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"

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)