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: "towards deep semantic analysis of hashtags"
  Abstract: Hashtags are semantico-syntactic constructs used across various social networking and microblogging platforms to enable users to start a topic specific discussion or classify a post into a desired category. Segmenting and linking the entities present within the hashtags could therefore help in better understanding and extraction of information shared across the social media. However, due to lack of space delimiters in the hashtags (e.g #nsavssnowden), the segmentation of hashtags into constituent entities ("NSA" and "Edward Snowden" in this case) is not a trivial task. Most of the current state-of-the-art social media analytics systems like Sentiment Analysis and Entity Linking tend to either ignore hashtags, or treat them as a single word. In this paper, we present a context aware approach to segment and link entities in the hashtags to a knowledge base (KB) entry, based on the context within the tweet. Our approach segments and links the entities in hashtags such that the coherence between hashtag semantics and the tweet is maximized. To the best of our knowledge, no existing study addresses the issue of linking entities in hashtags for extracting semantic information. We evaluate our method on two different datasets, and demonstrate the effectiveness of our technique in improving the overall entity linking in tweets via additional semantic information provided by segmenting and linking entities in a hashtag.

Connected through hyperedge HE-1 (induced by source paper "char rnn and active learning for hashtag segmentation"):
  with members:
    - "plusemo2vec at semeval 2018 task 1 exploiting emotion knowledge from emoji and hashtags"
    - "an end to end neural natural language interface for databases"
    - "universal word segmentation implementation and interpretation"
    - "deep active learning for named entity recognition"
    - "a gap based framework for chinese word segmentation via very deep convolutional networks"
    - "attention is all you need"
    - "fast and accurate neural word segmentation for chinese"
    - "neural word segmentation learning for chinese"

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"
    - "emo love and god making sense of urban dictionary a crowd sourced online dictionary"
    - "tweet2vec character based distributed representations for social media"
    - "adam a method for stochastic optimization"
    - "empirical methods for compound splitting"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-3 (induced by source paper "real time automatic word segmentation for user generated text"):
  with members:
    - "neural word segmentation learning for chinese"
    - "convolutional neural networks for sentence classification"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "distributed representations of words and phrases and their compositionality"
    - "mostly unsupervised statistical segmentation of japanese kanji sequences"

Connected through hyperedge HE-4 (induced by source paper "semantic annotation for microblog topics using wikipedia temporal information"):
  with members:
    - "hedera scalable indexing and exploring entities in wikipedia revision history"
    - "dynamical classes of collective attention in twitter"
    - "fast and accurate annotation of short texts with wikipedia pages"

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.IR(Information Retrieval)