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: "olive oil is made of olives baby oil is made for babies interpreting noun compounds using paraphrases in a neural model"
  Abstract: Automatic interpretation of the relation between the constituents of a noun compound, e.g. olive oil (source) and baby oil (purpose) is an important task for many NLP applications. Recent approaches are typically based on either noun-compound representations or paraphrases. While the former has initially shown promising results, recent work suggests that the success stems from memorizing single prototypical words for each relation. We explore a neural paraphrasing approach that demonstrates superior performance when such memorization is not possible.

Connected through hyperedge HE-1 (induced by source paper "still a pain in the neck evaluating text representations on lexical composition"):
  with members:
    - "semeval 2013 task 4 free paraphrases of noun compounds"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "linguistic knowledge and transferability of contextual representations"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "word sense induction with neural bilm and symmetric patterns"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "a systematic comparison of english noun compound representations"):
  with members:
    - "still a pain in the neck evaluating text representations on lexical composition"
    - "allennlp a deep semantic natural language processing platform"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "transfer and multi task learning for noun noun compound interpretation"):
  with members:
    - "paraphrase to explicate revealing implicit noun compound relations"
    - "supervised and unsupervised transfer learning for question answering"
    - "deep multitask learning for semantic dependency parsing"
    - "when is multitask learning effective semantic sequence prediction under varying data conditions"
    - "how transferable are neural networks in nlp applications"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "paraphrase to explicate revealing implicit noun compound relations"):
  with members:
    - "semeval 2013 task 4 free paraphrases of noun compounds"
    - "large scale noun compound interpretation using bootstrapping and the web as a corpus"

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)