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 "lanequest an accurate and energy efficient lane detection system"):
  Abstract: Current outdoor localization techniques fail to provide the required accuracy for estimating the car's lane. In this paper, we present LaneQuest: a system that leverages the ubiquitous and low-energy inertial sensors available in commodity smart-phones to provide an accurate estimate of the car's current lane. LaneQuest leverages hints from the phone sensors about the surrounding environment to detect the car's lane. For example, a car making a right turn most probably will be in the right-most lane, a car passing by a pothole will be in a specific lane, and the car's angular velocity when driving through a curve reflects its lane. Our investigation shows that there are amble opportunities in the environment, i.e. lane “anchors”, that provide cues about the car's lane. To handle the ambiguous location, sensors noise, and fuzzy lane anchors; LaneQuest employs a novel probabilistic lane estimation algorithm. Furthermore, it uses an unsupervised crowd-sourcing approach to learn the position and lane-span distribution of the different lane-level anchors. Our evaluation results from implementation on different android devices and 260Km driving traces by 13 drivers in different cities shows that LaneQuest can detect the different lane-level anchors with an average precision and recall of more than 90%. This leads to an accurate detection of the exact car's lane position 80% of the time, increasing to 89% of the time to within one lane. This comes with a low-energy footprint, allowing LaneQuest to be implemented on the energy-constrained mobile devices.

Includes members:
  Member "map a crowd sensing system for automatic map semantics identification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (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-3 (induced by source paper "crescendo an infrastructure free ubiquitous cellular network based localization system")
  Member "cellsense an accurate energy efficient gsm positioning system":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "crescendo an infrastructure free ubiquitous cellular network based localization system")
  Member "cellsense a probabilistic rssi based gsm positioning system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "semmatch road semantics based accurate map matching for challenging positioning data")
  Member "a hidden markov model for localization using low end gsm cell phones":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transitlabel a crowd sensing system for automatic labeling of transit stations semantics")
  Member "markov localization for mobile robots in dynamic environments":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fault diagnosing slam for varying scale change detection")

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)