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: "unsupervised measure of word similarity how to outperform co occurrence and vector cosine in vsms"
  Abstract: In this paper, we claim that vector cosine, which is generally considered among the most efficient unsupervised measures for identifying word similarity in Vector Space Models, can be outperformed by an unsupervised measure that calculates the extent of the intersection among the most mutually dependent contexts of the target words. To prove it, we describe and evaluate APSyn, a variant of the Average Precision that, without any optimization, outperforms the vector cosine and the co-occurrence on the standard ESL test set, with an improvement ranging between +9.00% and +17.98%, depending on the number of chosen top contexts.

Connected through hyperedge HE-1 (induced by source paper "unsupervised hypernym detection by distributional inclusion vector embedding"):
  with members:
    - "hierarchical embeddings for hypernymy detection and directionality"
    - "specialising word vectors for lexical entailment"
    - "poincar e embeddings for learning hierarchical representations"
    - "hyperlex a large scale evaluation of graded lexical entailment"
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "lifted rule injection for relation embeddings"
    - "hypernyms under siege linguistically motivated artillery for hypernymy detection"
    - "improving hypernymy detection with an integrated path based and distributional method"

Connected through hyperedge HE-2 (induced by source paper "hierarchical embeddings for hypernymy detection and directionality"):
  with members:
    - "hyperlex a large scale evaluation of graded lexical entailment"
    - "hypernyms under siege linguistically motivated artillery for hypernymy detection"
    - "order embeddings of images and language"
    - "word representations via gaussian embedding"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-3 (induced by source paper "measuring thematic fit with distributional feature overlap"):
  with members:
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "testing apsyn against vector cosine on similarity estimation"
    - "what a nerd beating students and vector cosine in the esl and toefl datasets"
    - "improving zero shot learning by mitigating the hubness problem"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-4 (induced by source paper "testing apsyn against vector cosine on similarity estimation"):
  with members:
    - "what a nerd beating students and vector cosine in the esl and toefl datasets"
    - "efficient estimation of word representations in vector space"
    - "from frequency to meaning vector space models of semantics"
    - "mining the web for synonyms pmi ir versus lsa on toefl"

Connected through hyperedge HE-5 (induced by source paper "hypernyms under siege linguistically motivated artillery for hypernymy detection"):
  with members:
    - "representing verbs with rich contexts an evaluation on verb similarity"
    - "experiments with three approaches to recognizing lexical entailment"
    - "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)