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: "a scalable deep neural network architecture for multi building and multi floor indoor localization based on wi fi fingerprinting"
  Abstract: One of the key technologies for future large-scale location-aware services covering a complex of multi-story buildings is a scalable indoor localization technique. In this paper, we report the current status of our investigation on the use of deep neural networks (DNNs) for the scalable building/floor classification and floor-level position estimation based on Wi-Fi fingerprinting. Exploiting the hierarchical nature of the building/floor estimation and floor-level coordinates estimation of a location, we propose a new DNN architecture consisting of a stacked autoencoder for the reduction of feature space dimension and a feed-forward classifier for multi-label classification of building/floor/location, on which the multi-building and multi-floor indoor localization system based on Wi-Fi fingerprinting is built. We evaluate the performance of building/floor estimation and floor-level coordinates estimation of a given location using the UJIIndoorLoc dataset covering three buildings with four or five floors in the Jaume I University (UJI) campus, Spain. Experimental results demonstrate the feasibility of the proposed DNN-based indoor localization system, which can provide near state-of-the-art performance using a single DNN. The proposed scalable DNN architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting can achieve near state-of-the-art performance with just a single DNN and enables the implementation with lower complexity and energy consumption at mobile devices.

Connected through hyperedge HE-1 (induced by source paper "device free user authentication activity classification and tracking using passive wi fi sensing a deep learning based approach"):
  with members:
    - "activation functions comparison of trends in practice and research for deep learning"
    - "densely connected convolutional networks"
    - "csi based fingerprinting for indoor localization a deep learning approach"
    - "adam a method for stochastic optimization"
    - "two stream convolutional networks for action recognition in videos"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "convolutional mixture density recurrent neural network for predicting user location with wifi fingerprints"):
  with members:
    - "low effort place recognition with wifi fingerprints using deep learning"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-3 (induced by source paper "supervised and semi supervised deep probabilistic models for indoor positioning problems"):
  with members:
    - "low effort place recognition with wifi fingerprints using deep learning"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-4 (induced by source paper "hapi a robust pseudo 3d calibration free wifi based indoor localization system"):
  with members:
    - "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"
    - "map a crowd sensing system for automatic map semantics identification"

Connected through hyperedge HE-5 (induced by source paper "hybrid building floor classification and location coordinates regression using a single input and multi output deep neural network for large scale indoor localization based on wi fi fingerprinting"):
  with members:
    - "large scale location aware services in access hierarchical building floor classification and location estimation using wi fi fingerprinting based on deep neural networks"
    - "low effort place recognition with wifi fingerprints using deep learning"

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)