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: "performance of social network sensors during hurricane sandy"
  Abstract: Information flow during catastrophic events is a critical aspect of disaster management. Modern communication platforms, in particular online social networks, provide an opportunity to study such flow and derive early-warning sensors, thus improving emergency preparedness and response. Performance of the social networks sensor method, based on topological and behavioral properties derived from the “friendship paradox”, is studied here for over 50 million Twitter messages posted before, during, and after Hurricane Sandy. We find that differences in users’ network centrality effectively translate into moderate awareness advantage (up to 26 hours); and that geo-location of users within or outside of the hurricane-affected area plays a significant role in determining the scale of such an advantage. Emotional response appears to be universal regardless of the position in the network topology, and displays characteristic, easily detectable patterns, opening a possibility to implement a simple “sentiment sensing” technique that can detect and locate disasters.

Connected through hyperedge HE-1 (induced by source paper "mining social media data for biomedical signals and health related behavior"):
  with members:
    - "singular value decomposition and neural networks"
    - "anatomy of an online misinformation network"
    - "human sexual cycles are driven by culture and match collective moods"
    - "limited individual attention and online virality of low quality information"
    - "quantified self meets social media sharing of weight updates on twitter"
    - "the rise of social bots"
    - "learning deep structure preserving image text embeddings"
    - "insights from machine learned diet success prediction"

Connected through hyperedge HE-2 (induced by source paper "a practical guide for the effective evaluation of twitter user geolocation"):
  with members:
    - "a survey of location prediction on twitter"
    - "semi supervised user geolocation via graph convolutional networks"
    - "replicability analysis for natural language processing testing significance with multiple datasets"
    - "continuous representation of location for geolocation and lexical dialectology using mixture density networks"
    - "multiview deep learning for predicting twitter users location"
    - "confounds and consequences in geotagged twitter data"
    - "inferring the origin locations of tweets with quantitative confidence"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-3 (induced by source paper "social media usage patterns during natural hazards"):
  with members:
    - "information spreading during emergencies and anomalous events"
    - "online human bot interactions detection estimation and characterization"
    - "sifting robotic from organic text a natural language approach for detecting automation on twitter"

Connected through hyperedge HE-4 (induced by source paper "scaling laws in geo located twitter data"):
  with members:
    - "social sensing of floods in the uk"
    - "a survey of location inference techniques on twitter"
    - "confounds and consequences in geotagged twitter data"
    - "scaling of city attractiveness for foreign visitors through big data of human economical and social media activity"
    - "geo located twitter as proxy for global mobility patterns"
    - "big questions for social media big data representativeness validity and other methodological pitfalls"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"

Connected through hyperedge HE-5 (induced by source paper "joint inference of user community and interest patterns in social interaction networks"):
  with members:
    - "crisis communication patterns in social media during hurricane sandy"
    - "virality prediction and community structure in social networks"
    - "the author topic model for authors and documents"
    - "technological networks and the spread of computer viruses"

Connected through hyperedge HE-6 (induced by source paper "crisis communication patterns in social media during hurricane sandy"):
  with members:
    - "understanding information spreading in social media during hurricane sandy user activity and network properties"
    - "joint inference of user community and interest patterns in social interaction networks"
    - "analyzing social interaction networks from twitter for planned special events"
    - "time critical social mobilization"

Connected through hyperedge HE-7 (induced by source paper "understanding information spreading in social media during hurricane sandy user activity and network properties"):
  with members:
    - "virality prediction and community structure in social networks"
    - "time critical social mobilization"
    - "technological networks and the spread of computer viruses"

Connected through hyperedge HE-8 (induced by source paper "nowcasting disaster damage"):
  with members:
    - "the simple rules of social contagion"
    - "the digital evolution of occupy wall street"
    - "the geospatial characteristics of a social movement communication network"
    - "using friends as sensors to detect global scale contagious outbreaks"
    - "modeling the adoption of innovations in the presence of geographic and media influences"

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)