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: "gov2vec learning distributed representations of institutions and their legal text"
  Abstract: We compare policy differences across institutions by embedding representations of the entire legal corpus of each institution and the vocabulary shared across all corpora into a continuous vector space. We apply our method, Gov2Vec, to Supreme Court opinions, Presidential actions, and official summaries of Congressional bills. The model discerns meaningful differences between government branches. We also learn representations for more fine-grained word sources: individual Presidents and (2-year) Congresses. The similarities between learned representations of Congresses over time and sitting Presidents are negatively correlated with the bill veto rate, and the temporal ordering of Presidents and Congresses was implicitly learned from only text. With the resulting vectors we answer questions such as: how does Obama and the 113th House differ in addressing climate change and how does this vary from environmental or economic perspectives? Our work illustrates vector-arithmetic-based investigations of complex relationships between word sources based on their texts. We are extending this to create a more comprehensive legal semantic map.

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 "shift of perspective identification within legal cases"):
  with members:
    - "identifying relationships among sentences in court case transcripts using discourse relations"
    - "legal document retrieval using document vector embeddings and deep learning"
    - "fast approach to build an automatic sentiment annotator for legal domain using transfer learning"
    - "semi supervised instance population of an ontology using word vector embedding"
    - "synergistic union of word2vec and lexicon for domain specific semantic similarity"
    - "deriving a representative vector for ontology classes with instance word vector embeddings"

Connected through hyperedge HE-3 (induced by source paper "legal document retrieval using document vector embeddings and deep learning"):
  with members:
    - "semi supervised instance population of an ontology using word vector embedding"
    - "synergistic union of word2vec and lexicon for domain specific semantic similarity"
    - "deriving a representative vector for ontology classes with instance word vector embeddings"
    - "node2vec scalable feature learning for networks"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "fast approach to build an automatic sentiment annotator for legal domain using transfer learning"):
  with members:
    - "identifying relationships among sentences in court case transcripts using discourse relations"
    - "legal document retrieval using document vector embeddings and deep learning"
    - "zero shot learning through cross modal transfer"

Connected through hyperedge HE-5 (induced by source paper "synergistic union of word2vec and lexicon for domain specific semantic similarity"):
  with members:
    - "semi supervised instance population of an ontology using word vector embedding"
    - "deriving a representative vector for ontology classes with instance word vector embeddings"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "predicting and understanding law making with word vectors and an ensemble model"):
  with members:
    - "exploring limits to prediction in complex social systems"
    - "document classification by inversion of distributed language representations"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "predicting and understanding law making with machine learning"):
  with members:
    - "exploring limits to prediction in complex social systems"
    - "document classification by inversion of distributed language representations"
    - "distributed representations of words and phrases and their compositionality"

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)