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: "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.

Connected through hyperedge HE-1 (induced by source paper "crescendo an infrastructure free ubiquitous cellular network based localization system"):
  with members:
    - "the diversity and scale matter ubiquitous transportation mode detection using single cell tower information"
    - "lanequest an accurate and energy efficient lane detection system"
    - "map a crowd sensing system for automatic map semantics identification"
    - "dejavu an accurate energy efficient outdoor localization system"
    - "cellsense an accurate energy efficient gsm positioning system"

Connected through hyperedge HE-2 (induced by source paper "mining user behaviour from smartphone data a literature review"):
  with members:
    - "inferring transportation modes from gps trajectories using a convolutional neural network"
    - "trajectorynet an embedded gps trajectory representation for point based classification using recurrent neural networks"
    - "deeptransport learning spatial temporal dependency for traffic condition forecasting"
    - "smartphone based vehicle telematics a ten year anniversary"

Connected through hyperedge HE-3 (induced by source paper "trans sense real time transportation schedule estimation using smart phones"):
  with members:
    - "the diversity and scale matter ubiquitous transportation mode detection using single cell tower information"
    - "dejavu an accurate energy efficient outdoor localization system"
    - "cellsense an accurate energy efficient gsm positioning system"

Connected through hyperedge HE-4 (induced by source paper "smartphone based vehicle telematics a ten year anniversary"):
  with members:
    - "sensemycity crowdsourcing an urban sensor"
    - "smartloc sensing landmarks silently for smartphone based metropolitan localization"
    - "toward cloud based vehicular networks with efficient resource management"
    - "prediction expectation and surprise methods designs and study of a deployed traffic forecasting service"

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)