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: "cellsense an accurate energy efficient gsm positioning system"
  Abstract: Context-aware applications have been gaining huge interest in the last few years. With cell phones becoming ubiquitous computing devices, cell phone localization has become an important research problem. In this paper, we present CellSense, a prob- abilistic RSSI-based fingerprinting location determi- nation system for GSM phones. We discuss the chal- lenges of implementing a probabilistic fingerprinting localization technique in GSM networks and present the details of the CellSense systemand how it addresses these challenges. We then extend the proposed system using a hybrid technique that combines probabilistic and deterministic estimation to achieve both high ac- curacy and low computational overhead.Moreover, the accuracy of the hybrid technique is robust to changes in its parameter values. To evaluate our proposed system, we implemented CellSense on Android-based phones. Results from two different testbeds, represent- ing urban and rural environments, for three differ- ent cellular providers show that CellSense provides at least 108.57% enhancement in accuracy in rural areas and at least 89.03% in urban areas compared to the current state of the art RSSI-based GSM localization systems. In additional, the proposed hybrid technique provides more than 6 times and 5.4 times reduction in computational requirements compared to the state of the art RSSI-based GSM localization systems for the rural and urban testbeds respectively.We also evaluate the effect of changing the different system parameters on the accuracy-complexity tradeoff and how the cell towers density and fingerprint density affect the system performance.

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"
    - "dejavu an accurate energy efficient outdoor localization system"

Connected through hyperedge HE-2 (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"
    - "dejavu an accurate energy efficient outdoor localization system"

Connected through hyperedge HE-3 (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"
    - "dejavu an accurate energy efficient outdoor localization system"

Connected through hyperedge HE-4 (induced by source paper "channel charting locating users within the radio environment using channel state information"):
  with members:
    - "direct localization for massive mimo"
    - "fingerprinting based positioning in distributed massive mimo systems"
    - "5g ultra dense cellular networks"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "millimeter wave channel modeling and cellular capacity evaluation"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "fundamental limits of cooperation"

Connected through hyperedge HE-5 (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"
    - "dejavu an accurate energy efficient outdoor localization 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"

Connected through hyperedge HE-6 (induced by source paper "lanequest an accurate and energy efficient lane detection system"):
  with members:
    - "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"
    - "cellsense a probabilistic rssi based gsm positioning system"
    - "a hidden markov model for localization using low end gsm cell phones"
    - "markov localization for mobile robots in dynamic environments"

Connected through hyperedge HE-7 (induced by source paper "the diversity and scale matter ubiquitous transportation mode detection using single cell tower information"):
  with members:
    - "map a crowd sensing system for automatic map semantics identification"
    - "dejavu an accurate energy efficient outdoor localization 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"

Connected through hyperedge HE-8 (induced by source paper "map a crowd sensing system for automatic map semantics identification"):
  with members:
    - "dejavu an accurate energy efficient outdoor localization system"
    - "crowdinside automatic construction of indoor floorplans"
    - "ubiquitous indoor localization and worldwide automatic construction of floor plans"
    - "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"

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)