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 "a hidden markov model for localization using low end gsm cell phones"):
  Abstract: Research in location determination for GSM phones has gained interest recently as it enables a wide set of location based services. RSSI-based techniques have been the preferred method for GSM localization on the handset as RSSI information is available in all cell phones. Although the GSM standard allows for a cell phone to receive signal strength information from up to seven cell towers, many of today's cell phones are low-end phones, with limited API support, that gives only information about the associated cell tower. In addition, in many places in the world, the density of cell towers is very small and therefore, the available cell tower information for localization is very limited. This raises the challenge of accurately determining the cell phone location with very limited information, mainly the RSSI of the associated cell tower. In this paper we propose a Hidden Markov Model based solution that leverages the signal strength history from only the associated cell tower to achieve accurate GSM localization. We discuss the challenges of implementing our system and present the details of our system and how it addresses the challenges. To evaluate our proposed system, we implemented it on Androidbased phones. Results for two different testbeds, representing urban and rural environments, show that our system provides at least 156% enhancement in median error in rural areas and at least 68% enhancement in median error in urban areas compared to current RSSI-based GSM localization systems

Includes members:
  Member "cellsense a probabilistic rssi based gsm positioning system":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semmatch road semantics based accurate map matching for challenging positioning data")
  Member "gac energy efficient hybrid gps accelerometer compass gsm localization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semmatch road semantics based accurate map matching for challenging positioning data")

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.NI(Networking and Internet Architecture)