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 "experienced deep reinforcement learning with generative adversarial networks gans for model free ultra reliable low latency communication"):
  Abstract: In this paper, a novel experienced deep reinforcement learning (deep-RL) framework is proposed to provide model-free resource allocation for ultra reliable low latency communication (URLLC). The proposed, experienced deep-RL framework can guarantee high end-to-end reliability and low end-to-end latency, under explicit data rate constraints, for each wireless without any models of or assumptions on the users' traffic. In particular, in order to enable the deep-RL framework to account for extreme network conditions and operate in highly reliable systems, a new approach based on generative adversarial networks (GANs) is proposed. This GAN approach is used to pre-train the deep-RL framework using a mix of real and synthetic data, thus creating an experienced deep-RL framework that has been exposed to a broad range of network conditions. Formally, the URLLC resource allocation problem is posed as a power minimization problem under reliability, latency, and rate constraints. To solve this problem using experienced deep-RL, first, the rate of each user is determined. Then, these rates are mapped to the resource block and power allocation vectors of the studied wireless system. Finally, the end-to-end reliability and latency of each user are used as feedback to the deep-RL framework. It is then shown that at the fixed-point of the deep-RL algorithm, the reliability and latency of the users are near-optimal. Moreover, for the proposed GAN approach, a theoretical limit for the generator output is analytically derived. Simulation results show how the proposed approach can achieve near-optimal performance within the rate-reliability-latency region, depending on the network and service requirements. The results also show that the proposed experienced deep-RL framework is able to remove the transient training time that makes conventional deep-RL methods unsuitable for URLLC.

Includes members:
  Member "generative adversarial networks for distributed intrusion detection in the internet of things":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "brainstorming generative adversarial networks bgans towards multi agent generative models with distributed private datasets")
  Member "deep learning based end to end wireless communication systems with conditional gan as unknown channel":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dl based csi feedback and cooperative recovery in massive mimo")
  Member "a vision of 6g wireless systems applications trends technologies and open research problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")
  Member "joint user association and resource allocation optimization for ultra reliable low latency hetnets":
    also connected through other hyperedges:
      (none)
  Member "federated learning for ultra reliable low latency v2v communications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "robust deep reinforcement learning for security and safety in autonomous vehicle systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "survey of deep reinforcement learning for motion planning of autonomous vehicles")
  Member "risk aware resource allocation for urllc challenges and strategies with machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cellular traffic prediction and classification a comparative evaluation of lstm and arima")
  Member "ultra reliable and low latency vehicular transmission an extreme value theory approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimized age of information tail for ultra reliable low latency communications in vehicular networks")

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)