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: "interpretable charge predictions for criminal cases learning to generate court views from fact descriptions"
  Abstract: In this paper, we propose to study the problem of COURT VIEW GENeration from the fact description in a criminal case. The task aims to improve the interpretability of charge prediction systems and help automatic legal document generation. We formulate this task as a text-to-text natural language generation (NLG) problem. Sequenceto-sequence model has achieved cutting-edge performances in many NLG tasks. However, due to the non-distinctions of fact descriptions, it is hard for Seq2Seq model to generate charge-discriminative court views. In this work, we explore charge labels to tackle this issue. We propose a label-conditioned Seq2Seq model with attention for this problem, to decode court views conditioned on encoded charge labels. Experimental results show the effectiveness of our method.

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"
    - "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"
    - "secaps a sequence enhanced capsule model for charge prediction"

Connected through hyperedge HE-2 (induced by source paper "deep ranking based cost sensitive multi label learning for distant supervision relation extraction"):
  with members:
    - "jointly learning semantic parser and natural language generator via dual information maximization"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "semi supervised learning for neural keyphrase generation"
    - "encoding implicit relation requirements for relation extraction a joint inference approach"
    - "reinforcement learning for relation classification from noisy data"
    - "denoising distant supervision for relation extraction via instance level adversarial training"
    - "neural relation extraction via inner sentence noise reduction and transfer learning"

Connected through hyperedge HE-3 (induced by source paper "geosqa a benchmark for scenario based question answering in the geography domain at high school level"):
  with members:
    - "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"
    - "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-4 (induced by source paper "cail2019 scm a dataset of similar case matching in legal domain"):
  with members:
    - "neural legal judgment prediction in english"
    - "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"
    - "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-5 (induced by source paper "creating auxiliary representations from charge definitions for criminal charge prediction"):
  with members:
    - "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"
    - "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-6 (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"
    - "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"
    - "predicting the law area and decisions of french supreme court cases"

Connected through hyperedge HE-7 (induced by source paper "legal judgment prediction via multi perspective bi feedback network"):
  with members:
    - "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"
    - "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 "semi supervised learning for neural keyphrase generation"):
  with members:
    - "deep keyphrase generation"
    - "semi supervised multitask learning for sequence labeling"
    - "get to the point summarization with pointer generator networks"
    - "empower sequence labeling with task aware neural language model"
    - "language to logical form with neural attention"
    - "neural network based abstract generation for opinions and arguments"
    - "unsupervised pretraining for sequence to sequence learning"
    - "improving neural machine translation models with monolingual data"

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)