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 "federated learning for localization a privacy preserving crowdsourcing method"):
  Abstract: Received Signal Strength (RSS) fingerprint-based localization attracted a lot of research effort as well as cultivating many commercial applications of location-based services due to its low cost and ease of implementation. There are many studies exploring use of machine learning algorithms for RSS fingerprint-based localization. However, there are several problems due to the brittle nature of RSS which is susceptible to noise, dynamic environments, and hardware variations. These problems require frequent retraining of machine learning models with new data gathered by crowdsourcing. Although crowdsourcing is a good way to collect data, it has a privacy problem due to collection of raw data from participants. Federated learning enables to preserving privacy of the crowdsourcing participants by performing model training at the edge devices in a decentralized manner, data of participants is not exposed to centralized entity. This paper presents a novel method to improve the reliability and the robustness of RSS fingerprint-based localization while preserving privacy of the participants and the users by employing federated learning. Proposed method could achieve below 1 meters difference using federated learning compared to centralized learning for RSS fingerprint-based localization, and improved localization accuracy by 16% when used as a booster for centralized learning.

Includes members:
  Member "wireless network intelligence at the edge":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptive gradient sparsification for efficient federated learning an online learning approach")
  Member "federated learning strategies for improving communication efficiency":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "trends and advancements in deep neural network communication")
  Member "ai benchmark running deep neural networks on android smartphones":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "replacing mobile camera isp with a single deep learning model")
  Member "deep learning in mobile and wireless networking a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning")
  Member "a survey of indoor localization systems and technologies":
    also connected through other hyperedges:
      (none)
  Member "communication efficient learning of deep networks from decentralized data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "csi based fingerprinting for indoor localization a deep learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)