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: "still a pain in the neck evaluating text representations on lexical composition"
  Abstract: Building meaningful phrase representations is challenging because phrase meanings are not simply the sum of their constituent meanings. Lexical composition can shift the meanings of the constituent words and introduce implicit information. We tested a broad range of textual representations for their capacity to address these issues. We found that as expected, contextualized word representations perform better than static word embeddings, more so on detecting meaning shift than in recovering implicit information, in which their performance is still far from that of humans. Our evaluation suite, including 5 tasks related to lexical composition effects, can serve future research aiming to improve such representations.

Connected through hyperedge HE-1 (induced by source paper "olmpics on what language model pre training captures"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "visualizing and measuring the geometry of bert"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "do nlp models know numbers probing numeracy in embeddings"
    - "learning and evaluating general linguistic intelligence"
    - "diversify your datasets analyzing generalization via controlled variance in adversarial datasets"
    - "question answering is a format when is it useful"

Connected through hyperedge HE-2 (induced by source paper "diversify your datasets analyzing generalization via controlled variance in adversarial datasets"):
  with members:
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "multi task deep neural networks for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-3 (induced by source paper "a systematic comparison of english noun compound representations"):
  with members:
    - "allennlp a deep semantic natural language processing platform"
    - "olive oil is made of olives baby oil is made for babies interpreting noun compounds using paraphrases in a neural model"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "paraphrase to explicate revealing implicit noun compound relations"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "learning paraphrastic sentence embeddings from back translated bitext"
    - "enriching word vectors with subword information"

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)