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 "limits lightweight machine learning for iot systems with resource limitations"):
  Abstract: Exploiting big data knowledge on small devices will pave the way for building truly cognitive Internet of Things (IoT) systems. Although machine learning has led to great advancements for IoT-based data analytics, there remains a huge methodological gap for the deployment phase of trained machine learning models. For given resource-constrained platforms such as Microcontroller Units (MCUs), model choice and parametrization are typically performed based on heuristics or analytical models. However, these approaches are only able to provide rough estimates of the required system resources as they do not consider the interplay of hardware, compiler specific optimizations, and code dependencies. In this paper, we present the novel open source framework LIghtweight Machine learning for IoT Systems (LIMITS), which applies a platform-in-the-loop approach explicitly considering the actual compilation toolchain of the target IoT platform. LIMITS focuses on high level tasks such as experiment automation, platform-specific code generation, and sweet spot determination. The solid foundations of validated low-level model implementations are provided by the coupled well-established data analysis framework Waikato Environment for Knowledge Analysis (WEKA). We apply and validate LIMITS in two case studies focusing on cellular data rate prediction and radio-based vehicle classification, where we compare different learning models and real world IoT platforms with memory constraints from 16 kB to 4 MB and demonstrate its potential to catalyze the development of machine learning enabled IoT systems.

Includes members:
  Member "towards cooperative data rate prediction for future mobile and vehicular 6g networks":
    also connected through other hyperedges:
      (none)
  Member "a reinforcement learning approach for efficient opportunistic vehicle to cloud data transfer":
    also connected through other hyperedges:
      (none)
  Member "empirical analysis of client based network quality prediction in vehicular multi mno networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards cooperative data rate prediction for future mobile and vehicular 6g networks")
  Member "system of systems modeling analysis and optimization of hybrid vehicular traffic":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "data driven network simulation for performance analysis of anticipatory vehicular communication systems")
  Member "boosting vehicle to cloud communication by machine learning enabled context prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards cooperative data rate prediction for future mobile and vehicular 6g networks")
  Member "toward intelligent vehicular networks a machine learning framework":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep reinforcement learning for autonomous internet of things model applications and challenges")
  Member "fastdeepiot towards understanding and optimizing neural network execution time on mobile and embedded devices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning based pedestrian inertial navigation methods dataset and on device inference")
  Member "machine learning based uplink transmission power prediction for lte and upcoming 5g networks using passive downlink indicators":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "data driven network simulation for performance analysis of anticipatory vehicular communication systems")

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)