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: "network features based co hyponymy detection"
  Abstract: Distinguishing lexical relations has been a long term pursuit in natural language processing (NLP) domain. Recently, in order to detect lexical relations like hypernymy, meronymy, co-hyponymy etc., distributional semantic models are being used extensively in some form or the other. Even though a lot of efforts have been made for detecting hypernymy relation, the problem of co-hyponymy detection has been rarely investigated. In this paper, we are proposing a novel supervised model where various network measures have been utilized to identify co-hyponymy relation with high accuracy performing better or at par with the state-of-the-art models.

Connected through hyperedge HE-1 (induced by source paper "using distributional thesaurus embedding for co hyponymy detection"):
  with members:
    - "can network embedding of distributional thesaurus be combined with word vectors for better representation"
    - "struc2vec learning node representations from structural identity"
    - "hierarchical embeddings for hypernymy detection and directionality"
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "nine features in a random forest to learn taxonomical semantic relations"
    - "node2vec scalable feature learning for networks"
    - "hypernyms under siege linguistically motivated artillery for hypernymy detection"
    - "line large scale information network embedding"

Connected through hyperedge HE-2 (induced by source paper "detecting reliable novel word senses a network centric approach"):
  with members:
    - "dynamic word embeddings for evolving semantic discovery"
    - "on the linearity of semantic change investigating meaning variation via dynamic graph models"
    - "cultural shift or linguistic drift comparing two computational measures of semantic change"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "statistically significant detection of linguistic change"
    - "that s sick dude automatic identification of word sense change across different timescales"

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)