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 "semmatch road semantics based accurate map matching for challenging positioning data"):
  Abstract: Map matching has been used to reduce the noisiness of the location estimates by aligning them to the road network on a digital map. A growing number of applications, e.g. energy-efficient localization and cellular provider side localization, depend on the availability of only sparse and coarse-grained positioning data; leading to a challenging map matching process. #R##N#In this paper, we present semMatch: a system that can provide accurate HMM-based map matching for challenging positioning traces. semMatch leverages the smartphone's inertial sensors to detect different road semantics; such as speed bumps, tunnels, and turns; and uses them in a mathe-matically-principled way as hints to overcome the sparse, noisy, and coarse-grained input positioning data, improving the HMM map matching accuracy and efficiency. To do that, semMatch applies a series of preprocessing modules to handle the noisy locations. The filtered location data is then processed by the core of semMatch system using a novel incremental HMM algorithm that combines a semantics-enriched digital map and the car's ambient road semantics in its estimation process. #R##N#We have evaluated semMatch using traces collected from different cities covering more than 150km under different harsh scenarios including coarse-grained cellular-based positioning data, sparse GPS traces with extremely low sampling rate, and noisy traces with a large number of back-and-force transitions. The results show that semMatch significantly outperforms traditional map matching algorithms under all scenarios, with an enhancement of at least 416% and 894% in precision and recall respectively in the most difficult cases. This highlights its promise as a next generation map matching algorithm for challenging environments.

Includes members:
  Member "lanequest an accurate and energy efficient lane detection system":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "real time lane id estimation using recurrent neural networks with dual convention")
  Member "map a crowd sensing system for automatic map semantics identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (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-4 (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-5 (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-6 (induced by source paper "lanequest an accurate and energy efficient lane detection system")
  Member "gac energy efficient hybrid gps accelerometer compass gsm localization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the diversity and scale matter ubiquitous transportation mode detection using single cell tower information")
  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 "transitlabel a crowd sensing system for automatic labeling of transit stations semantics")

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)