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: "the diversity and scale matter ubiquitous transportation mode detection using single cell tower information"
  Abstract: Detecting the transportation mode of a user is important for a wide range of applications. While a number of recent systems addressed the transportation mode detection problem using the ubiquitous mobile phones, these studies either leverage GPS, the inertial sensors, and/or multiple cell towers information. However, these different phone sensors have high energy consumption, limited to a small subset of phones (e.g. high-end phones or phones that support neighbouring cell tower information), cannot work in certain areas (e.g. inside tunnels for GPS), and/or work only from the user side. #R##N#In this paper, we present a transportation mode detection system, MonoSense, that leverages the phone serving cell information only. The basic idea is that the phone speed can be correlated with features extracted from both the serving cell tower ID and the received signal strength from it. To achieve high detection accuracy with this limited information, MonoSense leverages diversity along multiple axes to extract novel features. Specifically, MonoSense extracts features from both the time and frequency domain information available from the serving cell tower over different sliding widow sizes. More importantly, we show also that both the logarithmic and linear RSS scales can provide different information about the movement of a phone, further enriching the feature space and leading to higher accuracy. #R##N#Evaluation of MonoSense using 135 hours of cellular traces covering 485 km and collected by four users using different Android phones shows that it can achieve an average precision and recall of 89.26% and 89.84% respectively in differentiating between the stationary, walking, and driving modes using only the serving cell tower information, highlighting MonoSense ability to enable a wide set of intelligent transportation applications.

Connected through hyperedge HE-1 (induced by source paper "crescendo an infrastructure free ubiquitous cellular network based localization system"):
  with members:
    - "semmatch road semantics based accurate map matching for challenging positioning data"
    - "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 "trans sense real time transportation schedule estimation using smart phones"):
  with members:
    - "semmatch road semantics based accurate map matching for challenging positioning data"
    - "dejavu an accurate energy efficient outdoor localization system"
    - "cellsense an accurate energy efficient gsm positioning system"

Connected through hyperedge HE-3 (induced by source paper "power saving transportation mode identification for large scale applications"):
  with members:
    - "recognizing activities and spatial context using wearable sensors"

Connected through hyperedge HE-4 (induced by source paper "transitlabel a crowd sensing system for automatic labeling of transit stations semantics"):
  with members:
    - "a robust zero calibration rf based localization system for realistic environments"
    - "ubibreathe a ubiquitous non invasive wifi based breathing estimator"
    - "map a crowd sensing system for automatic map semantics identification"
    - "it s the human that matters accurate user orientation estimation for mobile computing applications"
    - "dejavu an accurate energy efficient outdoor localization system"
    - "crowdinside automatic construction of indoor floorplans"
    - "a hidden markov model for localization using low end gsm cell phones"

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.NI(Networking and Internet Architecture)