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: "csi based fingerprinting for indoor localization a deep learning approach"
  Abstract: With the fast growing demand of location-based services in indoor environments, indoor positioning based on fingerprinting has attracted a lot of interest due to its high accuracy. In this paper, we present a novel deep learning based indoor fingerprinting system using Channel State Information (CSI), which is termed DeepFi. Based on three hypotheses on CSI, the DeepFi system architecture includes an off-line training phase and an on-line localization phase. In the off-line training phase, deep learning is utilized to train all the weights of a deep network as fingerprints. Moreover, a greedy learning algorithm is used to train the weights layer-by-layer to reduce complexity. In the on-line localization phase, we use a probabilistic method based on the radial basis function to obtain the estimated location. Experimental results are presented to confirm that DeepFi can effectively reduce location error compared with three existing methods in two representative indoor environments.

Connected through hyperedge HE-1 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "realtime scheduling and power allocation using deep neural networks"
    - "over the air deep learning based radio signal classification"
    - "active learning for wireless iot intrusion detection"
    - "electrosense open and big spectrum data"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "adversarial attacks on deep learning based radio signal classification"

Connected through hyperedge HE-2 (induced by source paper "federated learning for localization a privacy preserving crowdsourcing method"):
  with members:
    - "wireless network intelligence at the edge"
    - "federated learning strategies for improving communication efficiency"
    - "ai benchmark running deep neural networks on android smartphones"
    - "deep learning in mobile and wireless networking a survey"
    - "a survey of indoor localization systems and technologies"
    - "communication efficient learning of deep networks from decentralized data"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "peregrine network localization and navigation with scalable inference and efficient operation"):
  with members:
    - "pricing mechanisms for crowd sensed spatial statistics based radio mapping"
    - "data collection and wireless communication in internet of things iot using economic analysis and pricing models a survey"
    - "distributed maximum likelihood sensor network localization"
    - "cramer rao bounds for joint rss doa based primary user localization in cognitive radio networks"
    - "sigma point belief propagation"
    - "fundamental limits of wideband localization part ii cooperative networks"
    - "fundamental limits of wideband localization part i a general framework"

Connected through hyperedge HE-4 (induced by source paper "thirty years of machine learning the road to pareto optimal next generation wireless networks"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "an introduction to deep learning for the physical layer"
    - "budget constrained multi armed bandits with multiple plays"
    - "unsupervised machine learning for networking techniques applications and research challenges"
    - "a survey of multi objective optimization in wireless sensor networks metrics algorithms and open problems"
    - "recent trends in deep learning based natural language processing"

Connected through hyperedge HE-5 (induced by source paper "machine learning for wireless communications in the internet of things a comprehensive survey"):
  with members:
    - "helper heterogeneous efficient low power radio for enabling ad hoc emergency public safety networks"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "over the air deep learning based radio signal classification"
    - "a non parametric multi stage learning framework for cognitive spectrum access in iot networks"
    - "qarc video quality aware rate control for real time video streaming based on deep reinforcement learning"
    - "cognitive non orthogonal multiple access with cooperative relaying a new wireless frontier for 5g spectrum sharing"
    - "a survey of machine and deep learning methods for internet of things iot security"
    - "fog computing survey of trends architectures requirements and research directions"

Connected through hyperedge HE-6 (induced by source paper "joint activity recognition and indoor localization with wifi fingerprints"):
  with members:
    - "continuous user authentication by contactless wireless sensing"
    - "csi net unified human body characterization and action recognition"
    - "embedding structured contour and location prior in siamesed fully convolutional networks for road detection"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "position tracking for virtual reality using commodity wifi"
    - "tensorbeat tensor decomposition for monitoring multi person breathing beats with commodity wifi"
    - "mask r cnn"

Connected through hyperedge HE-7 (induced by source paper "device free user authentication activity classification and tracking using passive wi fi sensing a deep learning based approach"):
  with members:
    - "a scalable deep neural network architecture for multi building and multi floor indoor localization based on wi fi fingerprinting"
    - "activation functions comparison of trends in practice and research for deep learning"
    - "densely connected convolutional networks"
    - "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-8 (induced by source paper "wi fi sensing applications and challenges"):
  with members:
    - "privacy aware smart metering progress and challenges"
    - "low latency heterogeneous networks with millimeter wave communications"
    - "tensorbeat tensor decomposition for monitoring multi person breathing beats with commodity wifi"
    - "monitoring breathing via signal strength in wireless networks"
    - "through wall tracking using variance based radio tomography networks"
    - "novelty and collective attention"

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)