human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "transitlabel a crowd sensing system for automatic labeling of transit stations semantics"):
  Abstract: We present TransitLabel, a crowd-sensing system for automatic enrichment of transit stations indoor floorplans with different semantics like ticket vending machines, entrance gates, drink vending machines, platforms, cars' waiting lines, restrooms, lockers, waiting (sitting) areas, among others. Our key observations show that certain passengers' activities (e.g., purchasing tickets, crossing entrance gates, etc) present identifiable signatures on one or more cell-phone sensors. TransitLabel leverages this fact to automatically and unobtrusively recognize different passengers' activities, which in turn are mined to infer their uniquely associated stations semantics. Furthermore, the locations of the discovered semantics are automatically estimated from the inaccurate passengers' positions when these semantics are identified. We evaluate TransitLabel through a field experiment in eight different train stations in Japan. Our results show that TransitLabel can detect the fine-grained stations semantics accurately with 7.7% false positive rate and 7.5% false negative rate on average. In addition, it can consistently detect the location of discovered semantics accurately, achieving an error within 2.5m on average for all semantics. Finally, we show that TransitLabel has a small energy footprint on cell-phones, could be generalized to other stations, and is robust to different phone placements; highlighting its promise as a ubiquitous indoor maps enriching service.

Includes members:
  Member "a robust zero calibration rf based localization system for realistic environments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "zero calibration device free localization for the iot based on participatory sensing")
  Member "the diversity and scale matter ubiquitous transportation mode detection using single cell tower information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "crescendo an infrastructure free ubiquitous cellular network based localization system")
  Member "ubibreathe a ubiquitous non invasive wifi based breathing estimator":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "besense leveraging wifi channel data and computational intelligence for behavior")
  Member "map a crowd sensing system for automatic map semantics identification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "coltrane convolutional trajectory network for deep map inference")
  Member "it s the human that matters accurate user orientation estimation for mobile computing applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "security properties of gait for mobile device pairing")
  Member "dejavu an accurate energy efficient outdoor localization system":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "crescendo an infrastructure free ubiquitous cellular network based localization system")
  Member "crowdinside automatic construction of indoor floorplans":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "a hidden markov model for localization using low end gsm cell phones":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a robust zero calibration rf based localization system for realistic environments")

Question: Please tell me which class the source paper 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)