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: "sensingkit evaluating the sensor power consumption in ios devices"
  Abstract: Today's smartphones come equipped with a range of advanced sensors capable of sensing motion, orientation, audio as well as environmental data with high accuracy. With the existence of application distribution channels such as the Apple App Store and the Google Play Store, researchers can distribute applications and collect large scale data in ways that previously were not possible. Motivated by the lack of a universal, multi-platform sensing library, in this work we present the design and implementation of SensingKit, an open-source continuous sensing system that supports both iOS and Android mobile devices. One of the unique features of SensingKit is the support of the latest beacon technologies based on Bluetooth Smart (BLE), such as iBeaconand Eddystone. We evaluate and compare the power consumption of each supported sensor individually, using an iPhone 5S device running on iOS 9. We believe that this platform will be beneficial to all researchers and developers who plan to use mobile sensing technology in large-scale experiments.

Connected through hyperedge HE-1 (induced by source paper "finding dory in the crowd detecting social interactions using multi modal mobile sensing"):
  with members:
    - "mobile sensor data anonymization"
    - "replacement autoencoder a privacy preserving algorithm for sensory data analysis"
    - "xgboost a scalable tree boosting system"
    - "learning human identity from motion patterns"
    - "tracking serendipitous interactions how individual cultures shape the office"

Connected through hyperedge HE-2 (induced by source paper "user environment detection with acoustic sensors embedded on mobile devices for the recognition of activities of daily living"):
  with members:
    - "data fusion on motion and magnetic sensors embedded on mobile devices for the identification of activities of daily living"
    - "pattern recognition techniques for the identification of activities of daily living using mobile device accelerometer"
    - "bigear inferring the ambient and emotional correlates from smartphone based acoustic big data"
    - "automatic environmental sound recognition performance versus computational cost"
    - "mobile device identification via sensor fingerprinting"

Connected through hyperedge HE-3 (induced by source paper "a multiple source framework for the identification of activities of daily living based on mobile device data"):
  with members:
    - "data fusion on motion and magnetic sensors embedded on mobile devices for the identification of activities of daily living"
    - "pattern recognition techniques for the identification of activities of daily living using mobile device accelerometer"
    - "user environment detection with acoustic sensors embedded on mobile devices for the recognition of activities of daily living"
    - "mobile device identification via sensor fingerprinting"

Connected through hyperedge HE-4 (induced by source paper "data fusion on motion and magnetic sensors embedded on mobile devices for the identification of activities of daily living"):
  with members:
    - "pattern recognition techniques for the identification of activities of daily living using mobile device accelerometer"
    - "mobile device identification via sensor fingerprinting"

Connected through hyperedge HE-5 (induced by source paper "pattern recognition techniques for the identification of activities of daily living using mobile device accelerometer"):
  with members:
    - "mobile device identification via sensor fingerprinting"

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)