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: "beating the news with embers forecasting civil unrest using open source indicators"
  Abstract: We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Unlike retrospective studies, EMBERS has been making forecasts into the future since Nov 2012 which have been (and continue to be) evaluated by an independent T&E team (MITRE). Of note, EMBERS has successfully forecast the uptick and downtick of incidents during the June 2013 protests in Brazil. We outline the system architecture of EMBERS, individual models that leverage specific data sources, and a fusion and suppression engine that supports trading off specific evaluation criteria. EMBERS also provides an audit trail interface that enables the investigation of why specific predictions were made along with the data utilized for forecasting. Through numerous evaluations, we demonstrate the superiority of EMBERS over baserate methods and its capability to forecast significant societal happenings.

Connected through hyperedge HE-1 (induced by source paper "titan a spatiotemporal feature learning framework for traffic incident duration prediction"):
  with members:
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-2 (induced by source paper "real time detection of content polluters in partially observable twitter networks"):
  with members:
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "online human bot interactions detection estimation and characterization"
    - "privacy and security in online social networks a survey"
    - "botornot a system to evaluate social bots"
    - "the darpa twitter bot challenge"
    - "the rise of social bots"
    - "making the most of tweet inherent features for social spam detection on twitter"

Connected through hyperedge HE-3 (induced by source paper "twitter reveals using twitter analytics to predict public protests"):
  with members:
    - "predicting crowd behavior with big public data"
    - "the digital evolution of occupy wall street"
    - "dynamical classes of collective attention in twitter"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-4 (induced by source paper "pachinko prediction a bayesian method for event prediction from social media data"):
  with members:
    - "real time detection of content polluters in partially observable twitter networks"
    - "divergent discourse between protests and counter protests blacklivesmatter and alllivesmatter"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-5 (induced by source paper "forecasting cyber attacks with imbalanced data sets and different time granularities"):
  with members:
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-6 (induced by source paper "using social media to predict the future a systematic literature review"):
  with members:
    - "what the language you tweet says about your occupation"
    - "mining online social data for detecting social network mental disorders"
    - "sentiment aggregate functions for political opinion polling using microblog streams"
    - "using user generated online photos to estimate and monitor air pollution in major cities"
    - "predicting crowd behavior with big public data"
    - "big questions for social media big data representativeness validity and other methodological pitfalls"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"

Connected through hyperedge HE-7 (induced by source paper "a generic framework for interesting subspace cluster detection in multi attributed networks"):
  with members:
    - "convexification of learning from constraints"
    - "outlier detection from network data with subnetwork interpretation"
    - "scalable anomaly ranking of attributed neighborhoods"
    - "approximation algorithms for model based compressive sensing"
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-8 (induced by source paper "guided deep list automating the generation of epidemiological line lists from open sources"):
  with members:
    - "characterizing diseases from unstructured text a vocabulary driven word2vec approach"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

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)