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: "learning from the news predicting entity popularity on twitter"
  Abstract: In this work, we tackle the problem of predicting entity popularity on Twitter based on the news cycle. We apply a supervised learn- ing approach and extract four types of features: (i) signal, (ii) textual, (iii) sentiment and (iv) semantic, which we use to predict whether the popularity of a given entity will be high or low in the following hours. We run several experiments on six different entities in a dataset of over 150M tweets and 5M news and obtained F1 scores over 0.70. Error analysis indicates that news perform better on predicting entity popularity on Twitter when they are the primary information source of the event, in opposition to events such as live TV broadcasts, political debates or football matches.

Connected through hyperedge HE-1 (induced by source paper "predicting tomorrow s headline using today s twitter deliberations"):
  with members:
    - "breaking the news first impressions matter on online news"
    - "characterizing the life cycle of online news stories using social media reactions"
    - "crowdsourcing a word emotion association lexicon"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-2 (induced by source paper "tracking the history and evolution of entities entity centric temporal analysis of large social media archives"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "tweetskb a public and large scale rdf corpus of annotated tweets"
    - "time aware and corpus specific entity relatedness"
    - "building and querying semantic layers for web archives extended version"
    - "balancing novelty and salience adaptive learning to rank entities for timeline summarization of high impact events"
    - "timeline generation tracking individuals on twitter"

Connected through hyperedge HE-3 (induced by source paper "tweetskb a public and large scale rdf corpus of annotated tweets"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "temporal effects on hashtag reuse in twitter a cognitive inspired hashtag recommendation approach"
    - "semantic annotation for microblog topics using wikipedia temporal information"

Connected through hyperedge HE-4 (induced by source paper "transportation in social media an automatic classifier for travel related tweets"):
  with members:
    - "feup at semeval 2017 task 5 predicting sentiment polarity and intensity with financial word embeddings"
    - "sentiment aggregate functions for political opinion polling using microblog streams"
    - "mining social media for open innovation in transportation systems"
    - "popmine tracking political opinion on the web"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "global entity ranking across multiple languages"):
  with members:
    - "high throughput and language agnostic entity disambiguation and linking on user generated data"
    - "klout score measuring influence across multiple social networks"
    - "entity ranking in wikipedia"

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.SI(Social and Information Networks)