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: "multi scale population and mobility estimation with geo tagged tweets"
  Abstract: Recent outbreaks of Ebola and Dengue viruses have again elevated the significance of the capability to quickly predict disease spread in an emergent situation. However, existing approaches usually rely heavily on the time-consuming census processes, or the privacy-sensitive call logs, leading to their unresponsive nature when facing the abruptly changing dynamics in the event of an outbreak. In this paper we study the feasibility of using large-scale Twitter data as a proxy of human mobility to model and predict disease spread. We report that for Australia, Twitter users' distribution correlates well the census-based population distribution, and that the Twitter users' travel patterns appear to loosely follow the gravity law at multiple scales of geographic distances, i.e. national level, state level and metropolitan level. The radiation model is also evaluated on this dataset though it has shown inferior fitness as a result of Australia's sparse population and large landmass. The outcomes of the study form the cornerstones for future work towards a model-based, responsive prediction method from Twitter data for disease spread.

Connected through hyperedge HE-1 (induced by source paper "creating full individual level location timelines from sparse social media data"):
  with members:
    - "understanding predictability and exploration in human mobility"

Connected through hyperedge HE-2 (induced by source paper "the use of public spaces in a medium sized city from twitter data to mobility patterns"):
  with members:
    - "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"

Connected through hyperedge HE-3 (induced by source paper "estimating local commuting patterns from geolocated twitter data"):
  with members:
    - "predicting human mobility through the assimilation of social media traces into mobility models"
    - "wikipedia traffic data and electoral prediction towards theoretically informed models"
    - "geo located twitter as proxy for global mobility patterns"
    - "uncovering patterns of inter urban trip and spatial interaction from social media check in data"
    - "where in the world are you geolocation and language identification in twitter"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"

Connected through hyperedge HE-4 (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-5 (induced by source paper "a new understanding of friendships in space complex networks meet twitter"):
  with members:
    - "geo located twitter as proxy for global mobility patterns"
    - "when is it biased assessing the representativeness of twitter s streaming api"
    - "friendship paradox redux your friends are more interesting than you"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "the anatomy of the facebook social graph"

Connected through hyperedge HE-6 (induced by source paper "where chicagoans tweet the most semantic analysis of preferential return locations of twitter users"):
  with members:
    - "a scalable framework for spatiotemporal analysis of location based social media data"

Connected through hyperedge HE-7 (induced by source paper "autonomous surveillance for biosecurity"):
  with members:
    - "guiding ebola patients to suitable health facilities an sms based approach"

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)