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 warning of human crowds based on query data from baidu maps analysis based on shanghai stampede"
  Abstract: Without sufficient preparation and on-site management, the mass-scale unexpected huge human crowd is a serious threat to public safety. A recent impressive tragedy is the 2014 Shanghai Stampede, where 36 people died and 49 were injured in celebration of the New Year’s Eve on December 31st 2014 in the Shanghai Bund. Due to the innately stochastic and complicated individual movement, it is not easy to predict collective gatherings, which can potentially leads to crowd events. In this chapter, with leveraging the big data generated on Baidu Maps, we propose a novel approach to early warning such potential crowd disasters, which has profound public benefits. An insightful observation is that, with the prevalence and convenience of web map service, users usually search on the Baidu Maps to plan a routine, which reveals the users’ future destinations. Therefore, aggregating users’ query data on Baidu Maps can obtain priori and indication information for estimating future human population in a specific area ahead of time. Our careful analysis and deep investigation on the Baidu Maps’ data on various events also demonstrate a strong correlation pattern between the number of map query and the number of positioning in an area. Based on such observation, we propose a decision method utilizing query data on Baidu Maps to invoke warnings for potential crowd events about 1~3 h in advance. Then we also construct a machine learning model with heterogeneous data (such as query data and positioning data) to quantitatively measure the risk of the potential crowd disasters. We evaluate the effectiveness of our methods on the data of Baidu Maps.

Connected through hyperedge HE-1 (induced by source paper "faster fusion analytics for public transport event response"):
  with members:
    - "robust commuter movement inference from connected mobile devices"
    - "stochastic on time arrival problem in transit networks"
    - "trajectorynet an embedded gps trajectory representation for point based classification using recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "the impact of position errors on crowd simulation"):
  with members:
    - "sensor fusion of camera gps and imu using fuzzy adaptive multiple motion models"
    - "crowd simulation modeling applied to emergency and evacuation simulations using multi agent systems"

Connected through hyperedge HE-3 (induced by source paper "on the statistical properties of viral misinformation in online social media"):
  with members:
    - "users polarization on facebook and youtube"
    - "hoaxy a platform for tracking online misinformation"
    - "personality traits and echo chambers on facebook"
    - "trend of narratives in the age of misinformation"
    - "debunking in a world of tribes"
    - "science vs conspiracy collective narratives in the age of misinformation"
    - "emotional dynamics in the age of misinformation"
    - "computational fact checking from knowledge networks"

Connected through hyperedge HE-4 (induced by source paper "measuring economic activities of china with mobile big data"):
  with members:
    - "store location selection via mining search query logs of baidu maps"
    - "transfer learning from deep features for remote sensing and poverty mapping"
    - "tracking employment shocks using mobile phone data"
    - "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)