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: "representing verbs with rich contexts an evaluation on verb similarity"
  Abstract: Several studies on sentence processing suggest that the mental lexicon keeps track of the mutual expectations between words. Current DSMs, however, represent context words as separate features, thereby loosing important information for word expectations, such as word interrelations. In this paper, we present a DSM that addresses this issue by defining verb contexts as joint syntactic dependencies. We test our representation in a verb similarity task on two datasets, showing that joint contexts achieve performances comparable to single dependencies or even better. Moreover, they are able to overcome the data sparsity problem of joint feature spaces, in spite of the limited size of our training corpus.

Connected through hyperedge HE-1 (induced by source paper "a structured distributional model of sentence meaning and processing"):
  with members:
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "measuring thematic fit with distributional feature overlap"
    - "question answering with grammatically interpretable representations"
    - "the mechanism of additive composition"
    - "is structure necessary for modeling argument expectations in distributional semantics"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "learning distributed representations of sentences from unlabelled data"
    - "skip thought vectors"

Connected through hyperedge HE-2 (induced by source paper "robust cross lingual hypernymy detection using dependency context"):
  with members:
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "hypernyms under siege linguistically motivated artillery for hypernymy detection"
    - "many languages one parser"
    - "yara parser a fast and accurate dependency parser"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-3 (induced by source paper "hypernyms under siege linguistically motivated artillery for hypernymy detection"):
  with members:
    - "unsupervised measure of word similarity how to outperform co occurrence and vector cosine in vsms"
    - "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)