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: "knowledge augmented language model and its application to unsupervised named entity recognition"
  Abstract: Traditional language models are unable to efficiently model entity names observed in text. All but the most popular named entities appear infrequently in text providing insufficient context. Recent efforts have recognized that context can be generalized between entity names that share the same type (e.g., \emph{person} or \emph{location}) and have equipped language models with access to an external knowledge base (KB). Our Knowledge-Augmented Language Model (KALM) continues this line of work by augmenting a traditional model with a KB. Unlike previous methods, however, we train with an end-to-end predictive objective optimizing the perplexity of text. We do not require any additional information such as named entity tags. In addition to improving language modeling performance, KALM learns to recognize named entities in an entirely unsupervised way by using entity type information latent in the model. On a Named Entity Recognition (NER) task, KALM achieves performance comparable with state-of-the-art supervised models. Our work demonstrates that named entities (and possibly other types of world knowledge) can be modeled successfully using predictive learning and training on large corpora of text without any additional information.

Connected through hyperedge HE-1 (induced by source paper "pretrained encyclopedia weakly supervised knowledge pretrained language model"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "language models as knowledge bases"
    - "latent retrieval for weakly supervised open domain question answering"
    - "data augmentation for bert fine tuning in open domain question answering"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "knowledge enhanced contextual word representations"
    - "multi passage bert a globally normalized bert model for open domain question answering"

Connected through hyperedge HE-2 (induced by source paper "named entity recognition only from word embeddings"):
  with members:
    - "design challenges and misconceptions in neural sequence labeling"
    - "reinforcement learning for relation classification from noisy data"
    - "ncrf an open source neural sequence labeling toolkit"
    - "unsupervised learning of syntactic structure with invertible neural projections"
    - "deep active learning for named entity recognition"
    - "swellshark a generative model for biomedical named entity recognition without labeled data"
    - "empower sequence labeling with task aware neural language model"
    - "neural architectures for named entity recognition"

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)