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: "dejavu an accurate energy efficient outdoor localization system"
  Abstract: We present Dejavu, a system that uses standard cell-phone sensors to provide accurate and energy-efficient outdoor localization suitable for car navigation. Our analysis shows that different road landmarks have a unique signature on cell-phone sensors; For example, going inside tunnels, moving over bumps, going up a bridge, and even potholes all affect the inertial sensors on the phone in a unique pattern. Dejavu employs a dead-reckoning localization approach and leverages these road landmarks, among other automatically discovered abundant virtual landmarks, to reset the accumulated error and achieve accurate localization. To maintain a low energy profile, Dejavu uses only energy-efficient sensors or sensors that are already running for other purposes. We present the design of Dejavu and how it leverages crowd-sourcing to automatically learn virtual landmarks and their locations. Our evaluation results from implementation on different android devices in both city and highway driving show that Dejavu can localize cell phones to within 8.4m median error in city roads and 16.6m on highways. Moreover, compared to GPS and other state-of-the-art systems, Dejavu can extend the battery lifetime by 347%, achieving even better localization results than GPS in the more challenging in-city driving conditions.

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"
    - "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"
    - "cellsense an accurate energy efficient gsm positioning system"

Connected through hyperedge HE-2 (induced by source paper "zero calibration device free localization for the iot based on participatory sensing"):
  with members:
    - "transitlabel a crowd sensing system for automatic labeling of transit stations semantics"
    - "a robust zero calibration rf based localization system for realistic environments"
    - "crowdinside automatic construction of indoor floorplans"
    - "aroma automatic generation of radio maps for localization systems"

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"
    - "semmatch road semantics based accurate map matching for challenging positioning data"
    - "cellsense an accurate energy efficient gsm positioning system"

Connected through hyperedge HE-4 (induced by source paper "transfer learning based outdoor position recovery with telco data"):
  with members:
    - "learn on source refine on target a model transfer learning framework with random forests"
    - "how transferable are features in deep neural networks"
    - "cellsense an accurate energy efficient gsm positioning system"

Connected through hyperedge HE-5 (induced by source paper "hapi a robust pseudo 3d calibration free wifi based indoor localization system"):
  with members:
    - "a scalable deep neural network architecture for multi building and multi floor indoor localization based on wi fi fingerprinting"
    - "vifi virtual fingerprinting wifi based indoor positioning via multi wall multi floor propagation model"
    - "csi based fingerprinting for indoor localization a deep learning approach"
    - "selective ap sequence based indoor localization without site survey"
    - "a robust zero calibration rf based localization system for realistic environments"
    - "lanequest an accurate and energy efficient lane detection system"
    - "an analysis of device free and device based wifi localization systems"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (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"
    - "the diversity and scale matter ubiquitous transportation mode detection using single cell tower information"
    - "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"
    - "crowdinside automatic construction of indoor floorplans"
    - "a hidden markov model for localization using low end gsm cell phones"

Connected through hyperedge HE-7 (induced by source paper "a robust zero calibration rf based localization system for realistic environments"):
  with members:
    - "transitlabel a crowd sensing system for automatic labeling of transit stations semantics"
    - "lanequest an accurate and energy efficient lane detection system"
    - "map a crowd sensing system for automatic map semantics identification"
    - "it s the human that matters accurate user orientation estimation for mobile computing applications"
    - "crowdinside automatic construction of indoor floorplans"
    - "a hidden markov model for localization using low end gsm cell phones"

Connected through hyperedge HE-8 (induced by source paper "semmatch road semantics based accurate map matching for challenging positioning data"):
  with members:
    - "lanequest an accurate and energy efficient lane detection system"
    - "map a crowd sensing system for automatic map semantics identification"
    - "it s the human that matters accurate user orientation estimation for mobile computing applications"
    - "cellsense an accurate energy efficient gsm positioning system"
    - "cellsense a probabilistic rssi based gsm positioning system"
    - "gac energy efficient hybrid gps accelerometer compass gsm localization"
    - "a hidden markov model for localization using low end gsm cell phones"
    - "on line viterbi algorithm for analysis of long biological sequences"

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)