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 "rf fingerprinting and deep learning assisted ue positioning in 5g"):
  Abstract: In this work, we investigate user equipment (UE) positioning assisted by deep learning (DL) in 5G and beyond networks. As compared to state of the art positioning algorithms used in today's networks, radio signal fingerprinting and machine learning (ML) assisted positioning requires smaller additional feedback overhead; and the positioning estimates are made directly inside the radio access network (RAN), thereby assisting in radio resource management. The conventional positioning algorithms will be used as back-up for the environments with high variability in conditions; but ML-assisted positioning serves as more efficient and simpler technique to provide better or similar positioning accuracy. In this regard, we study ML-assisted positioning methods and evaluate their performance using system level simulations for an outdoor scenario in Lincoln park Chicago. The study is based on the use of raytracing tools, a 3GPP 5G NR compliant system level simulator and DL framework to estimate positioning accuracy of the UE. The use of raytracing tool and system level simulator helps avoid expensive drive test measurements in practical scenarios. Our proposed mechanism is a first step towards more proactive mobility management in future networks. We evaluate and compare performance of various DL models and show mean positioning error in the range of 1-1.5m for the best DL configuration with appropriate system feature-modeling.

Includes members:
  Member "a very brief introduction to machine learning with applications to communication systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "perm2vec graph permutation selection for decoding of error correction codes using self attention")
  Member "deep learning in mobile and wireless networking a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning")
  Member "an introduction to deep learning for the physical layer":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")

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)