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 "human in the loop wireless communications machine learning and brain aware resource management"):
  Abstract: Human-centric applications such as virtual reality and immersive gaming will be central to the future wireless networks. Common features of such services include: a) their dependence on the human user's behavior and state, and b) their need for more network resources compared to conventional cellular applications. To successfully deploy such applications over wireless and cellular systems, the network must be made cognizant of not only the quality-of-service (QoS) needs of the applications, but also of the perceptions of the human users on this QoS. In this paper, by explicitly modeling the limitations of the human brain, a concrete measure for the delay perception of human users in a wireless network is introduced. Then, a novel learning method, called probability distribution identification, is proposed to find a probabilistic model for this delay perception based on the brain features of a human user. The proposed learning method uses both supervised and unsupervised learning techniques to build a Gaussian mixture model of the human brain features. Given a model for the delay perception of the human brain, a novel brain-aware resource management algorithm based on Lyapunov optimization is proposed for allocating radio resources to human users while minimizing the transmit power and taking into account the reliability of both machine type devices and human users. The proposed algorithm is shown to have a low complexity. Moreover, a closed-form relationship between the reliability measure and wireless physical layer metrics of the network is derived. Simulation results using real data from actual human users show that a brain-aware approach can yield savings of up to 78% in power compared to the system

Includes members:
  Member "dynamic mobility aware interference avoidance for aerial base stations in cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "drones in distress a game theoretic countermeasure for protecting uavs against gps spoofing")
  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 "beyond 5g with uavs foundations of a 3d wireless cellular network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint 3d maneuver and power adaptation for secure uav communication with comp reception")
  Member "robust deep reinforcement learning for security and safety in autonomous vehicle systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "survey of deep reinforcement learning for motion planning of autonomous vehicles")
  Member "virtual reality over wireless networks quality of service model and learning based resource management":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "risk based optimization of virtual reality over terahertz reconfigurable intelligent surfaces")
  Member "artificial neural networks based machine learning for wireless networks a tutorial":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fog computing systems state of the art research issues and future trends")
  Member "context aware small cell networks how social metrics improve wireless resource allocation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "network utility maximization based on incentive mechanism for truthful reporting of local information")
  Member "prospect pricing in cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "prospect theoretic analysis of privacy preserving mechanism")

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)