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: "an analysis of twitter messages in the 2011 tohoku earthquake"
  Abstract: Social media such as Facebook and Twitter have proven to be a useful resource to understand public opinion towards real world events. In this paper, we investigate over 1.5 million Twitter messages (tweets) for the period 9th March 2011 to 31st May 2011 in order to track awareness and anxiety levels in the Tokyo metropolitan district to the 2011 Tohoku Earthquake and subsequent tsunami and nuclear emergencies. These three events were tracked using both English and Japanese tweets. Preliminary results indicated: 1) close correspondence between Twitter data and earthquake events, 2) strong correlation between English and Japanese tweets on the same events, 3) tweets in the native language play an important roles in early warning, 4) tweets showed how quickly Japanese people’s anxiety returned to normal levels after the earthquake event. Several distinctions between English and Japanese tweets on earthquake events are also discussed. The results suggest that Twitter data can be used as a useful resource for tracking the public mood of populations affected by natural disasters as well as an early warning system.

Connected through hyperedge HE-1 (induced by source paper "twitter speaks a case of national disaster situational awareness"):
  with members:
    - "characterizing transgender health issues in twitter"
    - "social media analysis for organizations us northeastern public and state libraries case study"
    - "characterizing diseases and disorders in gay users tweets"
    - "mining public opinion about economic issues twitter and the u s presidential election"
    - "life never matters in the democrats mind examining strategies of retweeted social bots during a mass shooting event"
    - "what do the us west coast public libraries post on twitter"
    - "applications of deep learning and reinforcement learning to biological data"
    - "deep learning for iot big data and streaming analytics a survey"

Connected through hyperedge HE-2 (induced by source paper "newswire versus social media for disaster response and recovery"):
  with members:
    - "graph based semi supervised learning with convolution neural networks to classify crisis related tweets"
    - "domain adaptation with adversarial training and graph embeddings"
    - "linking tweets with monolingual and cross lingual news using transformed word embeddings"
    - "extractive summarization limits compression generalized model and heuristics"
    - "be in the know connecting news articles to relevant twitter conversations"

Connected through hyperedge HE-3 (induced by source paper "how do you relax when you re stressed a content analysis and infodemiology study of stress related tweets"):
  with members:
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-4 (induced by source paper "a review on application of data mining techniques to combat natural disasters"):
  with members:
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-5 (induced by source paper "a system for sensing human sentiments to augment a model for predicting rare lake events"):
  with members:
    - "early stage influenza detection from twitter"

Connected through hyperedge HE-6 (induced by source paper "facts and fabrications about ebola a twitter based study"):
  with members:
    - "enhancing twitter data analysis with simple semantic filtering example in tracking influenza like illnesses"

Connected through hyperedge HE-7 (induced by source paper "twitter sentiment analysis how to hedge your bets in the stock markets"):
  with members:
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-8 (induced by source paper "the royal birth of 2013 analysing and visualising public sentiment in the uk using twitter"):
  with members:
    - "twitter mood predicts the stock market"
    - "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.SI(Social and Information Networks)