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: "conditional generators of words definitions"
  Abstract: We explore recently introduced definition modeling technique that provided the tool for evaluation of different distributed vector representations of words through modeling dictionary definitions of words. In this work, we study the problem of word ambiguities in definition modeling and propose a possible solution by employing latent variable modeling and soft attention mechanisms. Our quantitative and qualitative evaluation and analysis of the model shows that taking into account words ambiguity and polysemy leads to performance improvement.

Connected through hyperedge HE-1 (induced by source paper "probing for semantic classes diagnosing the meaning content of word embeddings"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "semantic structure and interpretability of word embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "factors influencing the surprising instability of word embeddings"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "verb argument structure alternations in word and sentence embeddings"

Connected through hyperedge HE-2 (induced by source paper "mark my word a sequence to sequence approach to definition modeling"):
  with members:
    - "distributional semantics and linguistic theory"
    - "transformer xl attentive language models beyond a fixed length context"
    - "incorporating sememes into chinese definition modeling"
    - "xsense learning sense separated sparse representations and textual definitions for explainable word sense networks"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "scaling neural machine translation"
    - "deep contextualized word representations"
    - "universal sentence encoder"

Connected through hyperedge HE-3 (induced by source paper "improving interpretability of word embeddings by generating definition and usage"):
  with members:
    - "incorporating sememes into chinese definition modeling"
    - "xsense learning sense separated sparse representations and textual definitions for explainable word sense networks"
    - "deep contextualized word representations"
    - "semi supervised sequence modeling with cross view training"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "exploiting linguistic resources for neural machine translation using multi task learning"
    - "definition modeling learning to define word embeddings in natural language"

Connected through hyperedge HE-4 (induced by source paper "multi sense definition modeling using word sense decompositions"):
  with members:
    - "incorporating sememes into chinese definition modeling"
    - "a mixture model for learning multi sense word embeddings"
    - "learning to explain non standard english words and phrases"
    - "de conflated semantic representations"
    - "definition modeling learning to define word embeddings in natural language"
    - "linear algebraic structure of word senses with applications to polysemy"
    - "dynamic memory networks for visual and textual question answering"
    - "breaking sticks and ambiguities with adaptive skip gram"

Connected through hyperedge HE-5 (induced by source paper "incorporating sememes into chinese definition modeling"):
  with members:
    - "constituency parsing with a self attentive encoder"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "get to the point summarization with pointer generator networks"
    - "a structured self attentive sentence embedding"
    - "long short term memory networks for machine reading"
    - "a decomposable attention model for natural language inference"
    - "definition modeling learning to define word embeddings in natural language"

Connected through hyperedge HE-6 (induced by source paper "learning to describe phrases with local and global contexts"):
  with members:
    - "learning to explain non standard english words and phrases"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "neural machine translation by jointly learning to align and translate"
    - "a survey of paraphrasing and textual entailment methods"

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)