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: "automatic labelling of topics with neural embeddings"
  Abstract: Topics generated by topic models are typically represented as list of terms. To reduce the cognitive overhead of interpreting these topics for end-users, we propose labelling a topic with a succinct phrase that summarises its theme or idea. Using Wikipedia document titles as label candidates, we compute neural embeddings for documents and words to select the most relevant labels for topics. Compared to a state-of-the-art topic labelling system, our methodology is simpler, more efficient, and finds better topic labels.

Connected through hyperedge HE-1 (induced by source paper "sector a neural model for coherent topic segmentation and classification"):
  with members:
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "characterizing question facets for complex answer retrieval"
    - "structuring wikipedia articles with section recommendations"
    - "text segmentation as a supervised learning task"
    - "do convolutional networks need to be deep for text classification"
    - "benchmark for complex answer retrieval"
    - "getting deep recommenders fit bloom embeddings for sparse binary input output networks"
    - "wikireading a novel large scale language understanding task over wikipedia"

Connected through hyperedge HE-2 (induced by source paper "love me love me say and write that you love me enriching the wasabi song corpus with lyrics annotations"):
  with members:
    - "exploiting synchronized lyrics and vocal features for music emotion detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "music mood detection based on audio and lyrics with deep neural net"

Connected through hyperedge HE-3 (induced by source paper "reducing the effort for systematic reviews in software engineering"):
  with members:
    - "the evolution of ijhcs and chi a quantitative analysis"
    - "towards evidence based ontology for supporting systematic literature review"
    - "broadening the scope of nanopublications"

Connected through hyperedge HE-4 (induced by source paper "transfer topic labeling with domain specific knowledge base an analysis of uk house of commons speeches 1935 2014"):
  with members:
    - "scalable dynamic topic modeling with clustered latent dirichlet allocation clda"
    - "labeling topics with images using neural networks"
    - "source lda enhancing probabilistic topic models using prior knowledge sources"

Connected through hyperedge HE-5 (induced by source paper "using j k fold cross validation to reduce variance when tuning nlp models"):
  with members:
    - "bringing replication and reproduction together with generalisability in nlp three reproduction studies for target dependent sentiment analysis"

Connected through hyperedge HE-6 (induced by source paper "w2vlda almost unsupervised system for aspect based sentiment analysis"):
  with members:
    - "improving topic models with latent feature word representations"
    - "robust multilingual named entity recognition with shallow semi supervised features"
    - "ultradense word embeddings by orthogonal transformation"
    - "topic modeling over short texts by incorporating word embeddings"
    - "efficient estimation of word representations in vector space"
    - "measuring praise and criticism inference of semantic orientation from association"

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)