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: "monoise modeling noise using a modular normalization system"
  Abstract: We propose MoNoise: a normalization model focused on generalizability and efficiency, it aims at being easily reusable and adaptable. Normalization is the task of translating texts from a non- canonical domain to a more canonical domain, in our case: from social media data to standard language. Our proposed model is based on a modular candidate generation in which each module is responsible for a different type of normalization action. The most important generation modules are a spelling correction system and a word embeddings module. Depending on the definition of the normalization task, a static lookup list can be crucial for performance. We train a random forest classifier to rank the candidates, which generalizes well to all different types of normaliza- tion actions. Most features for the ranking originate from the generation modules; besides these features, N-gram features prove to be an important source of information. We show that MoNoise beats the state-of-the-art on different normalization benchmarks for English and Dutch, which all define the task of normalization slightly different.

Connected through hyperedge HE-1 (induced by source paper "towards robust word embeddings for noisy texts"):
  with members:
    - "joint aspect and polarity classification for aspect based sentiment analysis with end to end neural networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "towards twitter sentiment classification by multi level sentiment enriched word embeddings"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "improving text normalization by optimizing nearest neighbor matching"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-2 (induced by source paper "adapting sequence to sequence models for text normalization in social media"):
  with members:
    - "learning to compute word embeddings on the fly"
    - "improving text normalization by optimizing nearest neighbor matching"
    - "a nested attention neural hybrid model for grammatical error correction"
    - "get to the point summarization with pointer generator networks"
    - "bpemb tokenization free pre trained subword embeddings in 275 languages"
    - "synthetic and natural noise both break neural machine translation"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "achieving open vocabulary neural machine translation with hybrid word character models"

Connected through hyperedge HE-3 (induced by source paper "to normalize or not to normalize the impact of normalization on part of speech tagging"):
  with members:
    - "part of speech tagging for historical english"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "efficient estimation of word representations in vector space"

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)