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: "understanding state preferences with text as data introducing the un general debate corpus"
  Abstract: Every year at the United Nations (UN), member states deliver statements during the General Debate (GD) discussing major issues in world politics. These speeches provide invaluable information on governments’ perspectives and preferences on a wide range of issues, but have largely been overlooked in the study of international politics. This paper introduces a new dataset consisting of over 7300 country statements from 1970–2014. We demonstrate how the UN GD corpus (UNGDC) can be used as a resource from which country positions on different policy dimensions can be derived using text analytic methods. The article provides applications of these estimates, demonstrating the contribution the UNGDC can make to the study of international politics.

Connected through hyperedge HE-1 (induced by source paper "the dynamic embedded topic model"):
  with members:
    - "topic modeling in embedding spaces"
    - "improving topic models with latent feature word representations"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "metalda a topic model that efficiently incorporates meta information"
    - "jointly learning word embeddings and latent topics"
    - "nonparametric spherical topic modeling with word embeddings"
    - "neural variational inference for text processing"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "detecting local insights from global labels supervised zero shot sequence labeling via a convolutional decomposition"):
  with members:
    - "context is key grammatical error detection with contextual word representations"
    - "attention is not explanation"
    - "understanding convolutional neural networks for text classification"
    - "jointly learning to label sentences and tokens"
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised multitask learning for sequence labeling"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-3 (induced by source paper "multiplex communities and the emergence of international conflict"):
  with members:
    - "identification interpretability and bayesian word embeddings"
    - "hiding individuals and communities in a social network"
    - "the ground truth about metadata and community detection in networks"
    - "higher order organization of complex networks"
    - "learning scalable deep kernels with recurrent structure"
    - "community extraction in multilayer networks with heterogeneous community structure"
    - "a new framework for dynamical models on multiplex networks"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "sentiment and position taking analysis of parliamentary debates a systematic literature review"):
  with members:
    - "topic specific sentiment analysis can help identify political ideology"
    - "neural discourse structure for text categorization"
    - "stance and sentiment in tweets"
    - "bayesian optimization of text representations"
    - "get out the vote determining support or opposition from congressional floor debate transcripts"

Connected through hyperedge HE-5 (induced by source paper "the un security council debates 1995 2017"):
  with members:
    - "discursive landscapes and unsupervised topic modeling in ir a validation of text as data approaches through a new corpus of un security council speeches on afghanistan"

Connected through hyperedge HE-6 (induced by source paper "detecting policy preferences and dynamics in the un general debate with neural word embeddings"):
  with members:
    - "distributed representations of sentences and documents"
    - "word2vec parameter learning explained"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "efficient estimation of word representations in vector space"

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)