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: "detecting influenza outbreaks by analyzing twitter messages"
  Abstract: We analyze over 500 million Twitter messages from an eight month period and find that tracking a small number of flu-related keywords allows us to forecast future influenza rates with high accuracy, obtaining a 95% correlation with national health statistics. We then analyze the robustness of this approach to spurious keyword matches, and we propose a document classification component to filter these misleading messages. We find that this document classifier can reduce error rates by over half in simulated false alarm experiments, though more research is needed to develop methods that are robust in cases of extremely high noise.

Connected through hyperedge HE-1 (induced by source paper "extracting actionable knowledge from domestic violence discourses on social media"):
  with members:
    - "twitter mood predicts the stock market"
    - "data mining for actionable knowledge a survey"

Connected through hyperedge HE-2 (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"
    - "early stage influenza detection from twitter"

Connected through hyperedge HE-3 (induced by source paper "detecting events and patterns in large scale user generated textual streams with statistical learning methods"):
  with members:
    - "on voting intentions inference from twitter content a case study on uk 2010 general election"
    - "an analysis of twitter messages in the 2011 tohoku earthquake"
    - "omg u got flu analysis of shared health messages for bio surveillance"
    - "twitter mood predicts the stock market"
    - "bolasso model consistent lasso estimation through the bootstrap"
    - "social networks that matter twitter under the microscope"

Connected through hyperedge HE-4 (induced by source paper "enhancing twitter data analysis with simple semantic filtering example in tracking influenza like illnesses"):
  with members:
    - "omg u got flu analysis of shared health messages for bio surveillance"

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)