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 "risk based optimization of virtual reality over terahertz reconfigurable intelligent surfaces"):
  Abstract: In this paper, the problem of associating reconfigurable intelligent surfaces (RISs) to virtual reality (VR) users is studied for a wireless VR network. In particular, this problem is considered within a cellular network that employs terahertz (THz) operated RISs acting as base stations. To provide a seamless VR experience, high data rates and reliable low latency need to be continuously guaranteed. To address these challenges, a novel risk-based framework based on the entropic value-at-risk is proposed for rate optimization and reliability performance. Furthermore, a Lyapunov optimization technique is used to reformulate the problem as a linear weighted function, while ensuring that higher order statistics of the queue length are maintained under a threshold. To address this problem, given the stochastic nature of the channel, a policy-based reinforcement learning (RL) algorithm is proposed. Since the state space is extremely large, the policy is learned through a deep-RL algorithm. In particular, a recurrent neural network (RNN) RL framework is proposed to capture the dynamic channel behavior and improve the speed of conventional RL policy-search algorithms. Simulation results demonstrate that the maximal queue length resulting from the proposed approach is only within 1% of the optimal solution. The results show a high accuracy and fast convergence for the RNN with a validation accuracy of 91.92%.

Includes members:
  Member "on the reliability of wireless virtual reality at terahertz thz frequencies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")
  Member "a vision of 6g wireless systems applications trends technologies and open research problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")
  Member "reconfigurable intelligent surfaces for energy efficiency in wireless communication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mimo noma networks relying on reconfigurable intelligent surface a signal cancellation based design")
  Member "human in the loop wireless communications machine learning and brain aware resource management":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "patient centric hetnets powered by machine learning and big data analytics for 6g networks")
  Member "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "intelligent resource slicing for embb and urllc coexistence in 5g and beyond a deep reinforcement learning based approach")
  Member "virtual reality over wireless networks quality of service model and learning based resource management":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "prediction communication and computing duration optimization for vr video streaming")
  Member "rl 2 fast reinforcement learning via slow reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "artificial neural networks based machine learning for wireless networks a tutorial":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fog computing systems state of the art research issues and future trends")

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)