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 "information centric grant free access for iot fog networks edge vs cloud detection and learning"):
  Abstract: A multi-cell Fog-Radio Access Network (F-RAN) architecture is considered in which Internet of Things (IoT) devices periodically make noisy observations of a Quantity of Interest (QoI) and transmit using grant-free access in the uplink. The devices in each cell are connected to an Edge Node (EN), which may also have a finite-capacity fronthaul link to a central processor. In contrast to conventional information-agnostic protocols, the devices transmit using a Type-Based Multiple Access (TBMA) protocol that is tailored to enable the estimate of the field of correlated QoIs in each cell based on the measurements received from IoT devices. TBMA has been previously introduced in the single-cell scenarios as a bandwidth-efficient data collection method, and is here studied for the first time in a multi-cell F-RAN model as an instance of information-centric access protocols. To this end, in this paper, edge and cloud detection are designed and compared for a multi-cell system. In the former case, detection of the local QoI is done locally at each EN, while, with the latter, ENs forward the received signals, upon quantization, over the fronthaul links to the central processor that carries out centralized detection of all QoIs. Optimal model-based detectors are introduced and the resulting asymptotic behaviour of the probability of error at cloud and edge is derived. Then, for the scenario in which a statistical model is not available, data-driven edge and cloud detectors are discussed and evaluated in numerical results.

Includes members:
  Member "start making sense semantic plane filtering and control for post 5g connectivity":
    also connected through other hyperedges:
      (none)
  Member "cooperative deep reinforcement learning for multiple group nb iot networks optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous internet of things model applications and challenges")
  Member "non orthogonal multiplexing of ultra reliable and broadband services in fog radio architectures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "space diversity based grant free random access for critical and non critical iot services")
  Member "deep joint source channel coding for wireless image transmission":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep joint transmission recognition for power constrained iot devices")
  Member "5g wireless network slicing for embb urllc and mmtc a communication theoretic view":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "overhead aware design of reconfigurable intelligent surfaces in smart radio environments")
  Member "towards massive connectivity support for scalable mmtc communications in 5g networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressive massive access for internet of things cloud computing or fog computing")
  Member "fog aided wireless networks for content delivery fundamental latency tradeoffs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fundamental limits of wireless caching under mixed cacheable and uncacheable traffic")
  Member "a brief introduction to machine learning for engineers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "from learning to meta learning reduced training overhead and complexity for 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.IT(Information Theory)