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: "exploring universal patterns in human home work commuting from mobile phone data"
  Abstract: Home-work commuting has always attracted significant research attention because of its impact on human mobility. One of the key assumptions in this domain of study is the universal uniformity of commute times. However, a true comparison of commute patterns has often been hindered by the intrinsic differences in data collection methods, which make observation from different countries potentially biased and unreliable. In the present work, we approach this problem through the use of mobile phone call detail records (CDRs), which offers a consistent method for investigating mobility patterns in wholly different parts of the world. We apply our analysis to a broad range of datasets, at both the country (Portugal, Ivory Coast, and Saudi Arabia), and city (Boston) scale. Additionally, we compare these results with those obtained from vehicle GPS traces in Milan. While different regions have some unique commute time characteristics, we show that the home-work time distributions and average values within a single region are indeed largely independent of commute distance or country (Portugal, Ivory Coast, and Boston)–despite substantial spatial and infrastructural differences. Furthermore, our comparative analysis demonstrates that such distance-independence holds true only if we consider multimodal commute behaviors–as consistent with previous studies. In car-only (Milan GPS traces) and car-heavy (Saudi Arabia) commute datasets, we see that commute time is indeed influenced by commute distance. Finally, we put forth a testable hypothesis and suggest ways for future work to make more accurate and generalizable statements about human commute behaviors.

Connected through hyperedge HE-1 (induced by source paper "transfer learning from an auxiliary discriminative task for unsupervised anomaly detection"):
  with members:
    - "efficient gan based anomaly detection"
    - "high order graph convolutional recurrent neural network a deep learning framework for network scale traffic learning and forecasting"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "cnn features are also great at unsupervised classification"
    - "learning representations for automatic colorization"
    - "global multi layer network of human mobility"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"

Connected through hyperedge HE-2 (induced by source paper "assessing refugees integration via spatio temporal similarities of mobility and calling behaviors"):
  with members:
    - "detecting permanent and intermittent purchase hotspots via computational stigmergy"
    - "data for refugees the d4r challenge on mobility of syrian refugees in turkey"
    - "a stigmergy based analysis of city hotspots to discover trends and anomalies in urban transportation usage"
    - "studying migrant assimilation through facebook interests"
    - "spatio temporal data mining a survey of problems and methods"
    - "stigmergy based modeling to discover urban activity patterns from positioning data"
    - "crisis analytics big data driven crisis response"
    - "mobile communication signatures of unemployment"

Connected through hyperedge HE-3 (induced by source paper "optimizing city scale traffic flows through modeling isolated observations of vehicle movements"):
  with members:
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "d4d senegal the second mobile phone data for development challenge"
    - "continuous time bayesian networks"
    - "prediction expectation and surprise methods designs and study of a deployed traffic forecasting service"

Connected through hyperedge HE-4 (induced by source paper "assessing the quality of home detection from mobile phone data for official statistics"):
  with members:
    - "a comparison of spatial based targeted disease containment strategies using mobile phone data"
    - "an analytical framework to nowcast well being using mobile phone data"
    - "choosing the right home location definition method for the given dataset"
    - "d4d senegal the second mobile phone data for development challenge"
    - "delineating geographical regions with networks of human interactions in an extensive set of countries"

Connected through hyperedge HE-5 (induced by source paper "profiling presence patterns and segmenting user locations from cell phone data"):
  with members:
    - "analysis of tourism dynamics and special events through mobile phone metadata"
    - "choosing the right home location definition method for the given dataset"

Connected through hyperedge HE-6 (induced by source paper "mobinsight a framework using semantic neighborhood features for localized interpretations of urban mobility"):
  with members:
    - "urban pulse capturing the rhythm of cities"
    - "the death and life of great italian cities a mobile phone data perspective"
    - "not at home on the range peer production and the urban rural divide"
    - "are safer looking neighborhoods more lively a multimodal investigation into urban life"
    - "the digital life of walkable streets"
    - "the shortest path to happiness recommending beautiful quiet and happy routes in the city"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-7 (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"
    - "uncovering patterns of inter urban trip and spatial interaction from social media check in data"
    - "a scalable framework for spatiotemporal analysis of location based social media 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-8 (induced by source paper "impact of urban technology deployments on local commercial activity"):
  with members:
    - "predicting regional economic indices using big data of individual bank card transactions"
    - "urban magnetism through the lens of geo tagged photography"
    - "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"
    - "delineating geographical regions with networks of human interactions in an extensive set of countries"

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)