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: "uncovering patterns of inter urban trip and spatial interaction from social media check in data"
  Abstract: The article revisits spatial interaction and distance decay from the perspective of human mobility patterns and spatially-embedded networks based on an empirical data set. We extract nationwide inter-urban movements in China from a check-in data set that covers half a million individuals within 370 cities to analyze the underlying patterns of trips and spatial interactions. By fitting the gravity model, we find that the observed spatial interactions are governed by a power law distance decay effect. The obtained gravity model also closely reproduces the exponential trip displacement distribution. The movement of an individual, however, may not obey the same distance decay effect, leading to an ecological fallacy. We also construct a spatial network where the edge weights denote the interaction strengths. The communities detected from the network are spatially cohesive and roughly consistent with province boundaries. We attribute this pattern to different distance decay parameters between intra-province and inter-province trips.

Connected through hyperedge HE-1 (induced by source paper "depicting urban boundaries from a mobility network of spatial interactions a case study of great britain with geo located twitter data"):
  with members:
    - "explore spatiotemporal and demographic characteristics of human mobility via twitter a case study of chicago"
    - "geo located twitter as proxy for global mobility patterns"
    - "a scalable framework for spatiotemporal analysis of location based social media data"
    - "exploring universal patterns in human home work commuting from mobile phone data"
    - "delineating geographical regions with networks of human interactions in an extensive set of countries"
    - "a classification for community discovery methods in complex networks"

Connected through hyperedge HE-2 (induced by source paper "the network structure of visited locations according to geotagged social media photos"):
  with members:
    - "learning points and routes to recommend trajectories"
    - "yfcc100m the new data in multimedia research"
    - "urban magnetism through the lens of geo tagged photography"
    - "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"
    - "where in the world are you geolocation and language identification in twitter"
    - "multi scale population and mobility estimation with geo tagged tweets"
    - "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 "predicting human mobility through the assimilation of social media traces into mobility models"):
  with members:
    - "twitter as a source of global mobility patterns for social good"
    - "yfcc100m the new data in multimedia research"
    - "geo located twitter as proxy for global mobility patterns"

Connected through hyperedge HE-5 (induced by source paper "profiling underprivileged residents with mid term public transit smartcard data of beijing"):
  with members:
    - "the impact of social segregation on human mobility in developing and urbanized regions"
    - "uncovering patterns of inter urban trips and spatial interactions from check in data"

Connected through hyperedge HE-6 (induced by source paper "population spatialization and synthesis with open data"):
  with members:
    - "mapping block level urban areas for all chinese cities"
    - "automated identification and characterization of parcels aicp with openstreetmap and points of interest"

Connected through hyperedge HE-7 (induced by source paper "urban spatial temporal activity structures a new approach to inferring the intra urban functional regions via social media check in data"):
  with members:
    - "robust principal component analysis"

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)