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: "are safer looking neighborhoods more lively a multimodal investigation into urban life"
  Abstract: Policy makers, urban planners, architects, sociologists, and economists are interested in creating urban areas that are both lively and safe. But are the safety and liveliness of neighborhoods independent characteristics? Or are they just two sides of the same coin? In a world where people avoid unsafe looking places, neighborhoods that look unsafe will be less lively, and will fail to harness the natural surveillance of human activity. But in a world where the preference for safe looking neighborhoods is small, the connection between the perception of safety and liveliness will be either weak or nonexistent. In this paper we explore the connection between the levels of activity and the perception of safety of neighborhoods in two major Italian cities by combining mobile phone data (as a proxy for activity or liveliness) with scores of perceived safety estimated using a Convolutional Neural Network trained on a dataset of Google Street View images scored using a crowdsourced visual perception survey. We find that: (i) safer looking neighborhoods are more active than what is expected from their population density, employee density, and distance to the city centre; and (ii) that the correlation between appearance of safety and activity is positive, strong, and significant, for females and people over 50, but negative for people under 30, suggesting that the behavioral impact of perception depends on the demographic of the population. Finally, we use occlusion techniques to identify the urban features that contribute to the appearance of safety, finding that greenery and street facing windows contribute to a positive appearance of safety (in agreement with Oscar Newman's defensible space theory). These results suggest that urban appearance modulates levels of human activity and, consequently, a neighborhood's rate of natural surveillance.

Connected through hyperedge HE-1 (induced by source paper "facelift a transparent deep learning framework to beautify urban scenes"):
  with members:
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "disentangled person image generation"
    - "unsupervised cross domain image generation"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "deep learning the city quantifying urban perception at a global scale"
    - "context encoders feature learning by inpainting"

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 "the economic value of neighborhoods predicting real estate prices from the urban environment"):
  with members:
    - "the new urban success how culture pays"
    - "vision based real estate price estimation"
    - "image based appraisal of real estate properties"
    - "xgboost a scalable tree boosting system"
    - "the death and life of great italian cities a mobile phone data perspective"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep residual learning for image recognition"
    - "network lasso clustering and optimization in large graphs"

Connected through hyperedge HE-4 (induced by source paper "computer analysis of architecture using automatic image understanding"):
  with members:
    - "automatic prediction of building age from photographs"

Connected through hyperedge HE-5 (induced by source paper "using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale"):
  with members:
    - "deep learning the city quantifying urban perception at a global scale"
    - "unsupervised deep feature extraction for remote sensing image classification"
    - "aerial image geolocalization from recognition and matching of roads and intersections"
    - "deep residual learning for image recognition"
    - "wide area image geolocalization with aerial reference imagery"
    - "land use classification in remote sensing images by convolutional neural networks"
    - "deepsat a learning framework for satellite imagery"
    - "very deep convolutional networks for large scale image recognition"

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"
    - "the digital life of walkable streets"
    - "the shortest path to happiness recommending beautiful quiet and happy routes in the city"
    - "exploring universal patterns in human home work commuting from mobile phone data"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-7 (induced by source paper "viraliency pooling local virality"):
  with members:
    - "how to make an image more memorable a deep style transfer approach"
    - "em algorithms for weighted data clustering with application to audio visual scene analysis"
    - "deep learning the city quantifying urban perception at a global scale"
    - "understanding image virality"
    - "a neural algorithm of artistic style"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image recognition"

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.CY(Computers and Society)