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: "noun phrase co occurrence statistics for semi automatic semantic lexicon construction"
  Abstract: Generating semantic lexicons semi-automatically could be a great time saver, relative to creating them by hand. In this paper, we present an algorithm for extracting potential entries for a category from an on-line corpus, based upon a small set of exemplars. Our algorithm finds more correct terms and fewer incorrect ones than previous work in this area. Additionally, the entries that are generated potentially provide broader coverage of the category than would occur to an individual coding them by hand. Our algorithm finds many terms not included within Wordnet (many more than previous algorithms), and could be viewed as an ``enhancer'' of existing broad-coverage resources.

Connected through hyperedge HE-1 (induced by source paper "multi context term embeddings the use case of corpus based term set expansion"):
  with members:
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "term set expansion based nlp architect by intel ai lab"
    - "term set expansion based on multi context term embeddings an end to end workflow"
    - "distributional term set expansion"
    - "a deeper look into dependency based word embeddings"
    - "deep contextualized word representations"
    - "deep sets"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "measuring conceptual entanglement in collections of documents"):
  with members:
    - "quantum entanglement in concept combinations"
    - "meaning focused and quantum inspired information retrieval"

Connected through hyperedge HE-3 (induced by source paper "words of estimative correlation studying verbalizations of scatterplots"):
  with members:
    - "exploranative code quality documents"
    - "acquiring correct knowledge for natural language generation"

Connected through hyperedge HE-4 (induced by source paper "term set expansion based nlp architect by intel ai lab"):
  with members:
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "distributional term set expansion"
    - "deep sets"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "using contexts and constraints for improved geotagging of human trafficking webpages"):
  with members:
    - "information extraction in illicit web domains"

Connected through hyperedge HE-6 (induced by source paper "cause identification from aviation safety incident reports via weakly supervised semantic lexicon construction"):
  with members:
    - "machine learning in automated text categorization"

Connected through hyperedge HE-7 (induced by source paper "acquiring word meaning mappings for natural language interfaces"):
  with members:
    - "inducing a semantically annotated lexicon via em based clustering"
    - "committee based sample selection for probabilistic classifiers"
    - "a probabilistic approach to lexical semantic knowledge acquisition and structural disambiguation"

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)