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: "improving text normalization by optimizing nearest neighbor matching"
  Abstract: Text normalization is an essential task in the processing and analysis of social media that is dominated with informal writing. It aims to map informal words to their intended standard forms. Previously proposed text normalization approaches typically require manual selection of parameters for improved performance. In this paper, we present an automatic optimizationbased nearest neighbor matching approach for text normalization. This approach is motivated by the observation that text normalization is essentially a matching problem and nearest neighbor matching with an adaptive similarity function is the most direct procedure for it. Our similarity function incorporates weighted contributions of contextual, string, and phonetic similarity, and the nearest neighbor matching involves a minimum similarity threshold. These four parameters are tuned efficiently using grid search. We evaluate the performance of our approach on two benchmark datasets. The results demonstrate that parameter tuning on small sized labeled datasets produce state-of-the-art text normalization performances. Thus, this approach allows practically easy construction of evolving domain-specific normalization lexicons

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"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "opennmt open source toolkit for neural machine translation"

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"
    - "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"
    - "monoise modeling noise using a modular normalization system"
    - "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"

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)