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: "early stage influenza detection from twitter"
  Abstract: Influenza is an acute respiratory illness that occurs virtually every year and results in substantial disease, death and expense. Detection of Influenza in its earliest stage would facilitate timely action that could reduce the spread of the illness. Existing systems such as CDC and EISS which try to collect diagnosis data, are almost entirely manual, resulting in about two-week delays for clinical data acquisition. Twitter, a popular microblogging service, provides us with a perfect source for early-stage flu detection due to its real- time nature. For example, when a flu breaks out, people that get the flu may post related tweets which enables the detection of the flu breakout promptly. In this paper, we investigate the real-time flu detection problem on Twitter data by proposing Flu Markov Network (Flu-MN): a spatio-temporal unsupervised Bayesian algorithm based on a 4 phase Markov Network, trying to identify the flu breakout at the earliest stage. We test our model on real Twitter datasets from the United States along with baselines in multiple applications, such as real-time flu breakout detection, future epidemic phase prediction, or Influenza-like illness (ILI) physician visits. Experimental results show the robustness and effectiveness of our approach. We build up a real time flu reporting system based on the proposed approach, and we are hopeful that it would help government or health organizations in identifying flu outbreaks and facilitating timely actions to decrease unnecessary mortality.

Connected through hyperedge HE-1 (induced by source paper "automating dynamic consent decisions for the processing of social media data in health research"):
  with members:
    - "decision provenance harnessing data flow for accountable systems"
    - "some hci priorities for gdpr compliant machine learning"
    - "contextual consent ethical mining of social media for health research"
    - "instagram photos reveal predictive markers of depression"

Connected through hyperedge HE-2 (induced by source paper "contextual consent ethical mining of social media for health research"):
  with members:
    - "instagram photos reveal predictive markers of depression"

Connected through hyperedge HE-3 (induced by source paper "sample nlpde and nlode social media modeling of information transmission for infectious diseases case study ebola"):
  with members:
    - "modeling information diffusion in online social networks with partial differential equations"
    - "analysis of a heterogeneous social network of humans and cultural objects"
    - "a computer virus propagation model using delay differential equations with probabilistic contagion and immunity"
    - "inheritance patterns in citation networks reveal scientific memes"
    - "statistically significant detection of linguistic change"
    - "do retweets indicate interest trust agreement extended abstract"
    - "a cooperate defect model for the spread of deviant behavior in social networks"
    - "what they do in shadows twitter underground follower market"

Connected through hyperedge HE-4 (induced by source paper "defender detecting and forecasting epidemics using novel data analytics for enhanced response"):
  with members:
    - "the early bird catches the term combining twitter and news data for event detection and situational awareness"
    - "detecting influenza outbreaks by analyzing twitter messages"

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:
    - "an analysis of twitter messages in the 2011 tohoku earthquake"

Connected through hyperedge HE-6 (induced by source paper "overcoming data scarcity of twitter using tweets as bootstrap with application to autism related topic content analysis"):
  with members:
    - "using twitter to learn about the autism community"

Connected through hyperedge HE-7 (induced by source paper "the early bird catches the term combining twitter and news data for event detection and situational awareness"):
  with members:
    - "towards real time summarization of scheduled events from twitter streams"

Connected through hyperedge HE-8 (induced by source paper "information diffusion in social sensing"):
  with members:
    - "forecasting popularity of videos in youtube"
    - "interactive sensing and decision making in social networks"
    - "loopy belief propagation for approximate inference an empirical study"
    - "removal of data incest in multi agent social learning in social networks"
    - "twitter mood predicts the stock market"

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)