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: "emo love and god making sense of urban dictionary a crowd sourced online dictionary"
  Abstract: The Internet facilitates large-scale collaborative projects. The emergence of Web~2.0 platforms, where producers and consumers of content unify, has drastically changed the information market. On the one hand, the promise of the "wisdom of the crowd" has inspired successful projects such as Wikipedia, which has become the primary source of crowd-based information in many languages. On the other hand, the decentralized and often un-monitored environment of such projects may make them susceptible to systematic malfunction and misbehavior. In this work, we focus on Urban Dictionary, a crowd-sourced online dictionary. We combine computational methods with qualitative annotation and shed light on the overall features of Urban Dictionary in terms of growth, coverage and types of content. We measure a high presence of opinion-focused entries, as opposed to the meaning-focused entries that we expect from traditional dictionaries. Furthermore, Urban Dictionary covers many informal, unfamiliar words as well as proper nouns. There is also a high presence of offensive content, but highly offensive content tends to receive lower scores through the voting system. Our study highlights that Urban Dictionary has a higher content heterogeneity than found in traditional dictionaries, which poses challenges in terms in processing but also offers opportunities to analyze and track language innovation.

Connected through hyperedge HE-1 (induced by source paper "how to evaluate word representations of informal domain"):
  with members:
    - "adaptive region embedding for text classification"
    - "learning to explain non standard english words and phrases"
    - "tweet2vec character based distributed representations for social media"
    - "tweet2vec learning tweet embeddings using character level cnn lstm encoder decoder"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"
    - "evaluating informal domain word representations with urbandictionary"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-2 (induced by source paper "multi task pairwise neural ranking for hashtag segmentation"):
  with members:
    - "microblog hashtag generation via encoding conversation contexts"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "exploring the limits of weakly supervised pretraining"
    - "tweet2vec character based distributed representations for social media"
    - "towards deep semantic analysis of hashtags"
    - "adam a method for stochastic optimization"
    - "empirical methods for compound splitting"
    - "thumbs up sentiment classification using machine learning techniques"

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)