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: "urban pulse capturing the rhythm of cities"
  Abstract: Cities are inherently dynamic. Interesting patterns of behavior typically manifest at several key areas of a city over multiple temporal resolutions. Studying these patterns can greatly help a variety of experts ranging from city planners and architects to human behavioral experts. Recent technological innovations have enabled the collection of enormous amounts of data that can help in these studies. However, techniques using these data sets typically focus on understanding the data in the context of the city, thus failing to capture the dynamic aspects of the city. The goal of this work is to instead understand the city in the context of multiple urban data sets. To do so, we define the concept of an “urban pulse” which captures the spatio-temporal activity in a city across multiple temporal resolutions. The prominent pulses in a city are obtained using the topology of the data sets, and are characterized as a set of beats. The beats are then used to analyze and compare different pulses. We also design a visual exploration framework that allows users to explore the pulses within and across multiple cities under different conditions. Finally, we present three case studies carried out by experts from two different domains that demonstrate the utility of our framework.

Connected through hyperedge HE-1 (induced by source paper "urbanrhythm revealing urban dynamics hidden in mobility data"):
  with members:
    - "city of the people for the people sensing urban dynamics via social media interactions"
    - "on data driven saak transform"
    - "a saak transform approach to efficient scalable and robust handwritten digits recognition"
    - "joint unsupervised learning of deep representations and image clusters"
    - "automatic city region analysis for urban routing"

Connected through hyperedge HE-2 (induced by source paper "glog laplacian of gaussian for spatial pattern detection in spatio temporal data"):
  with members:
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-3 (induced by source paper "neuralcubes deep representations for visual data exploration"):
  with members:
    - "a model for learned bloom filters and related structures"
    - "rnnbow visualizing learning via backpropagation gradients in rnns"
    - "the case for learned index structures"
    - "dynamic routing between capsules"
    - "improving generalization performance by switching from adam to sgd"
    - "representation learning by learning to count"
    - "towards better analysis of deep convolutional neural networks"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"

Connected through hyperedge HE-4 (induced by source paper "mobinsight a framework using semantic neighborhood features for localized interpretations of urban mobility"):
  with members:
    - "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"
    - "exploring universal patterns in human home work commuting from mobile phone data"
    - "adadelta an adaptive learning rate method"

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)