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 "constrained deep reinforcement learning for energy sustainable multi uav based random access iot networks with noma"):
  Abstract: In this paper, we apply the Non-Orthogonal Multiple Access (NOMA) technique to improve the massive channel access of a wireless IoT network where solar-powered Unmanned Aerial Vehicles (UAVs) relay data from IoT devices to remote servers. Specifically, IoT devices contend for accessing the shared wireless channel using an adaptive $p$-persistent slotted Aloha protocol; and the solar-powered UAVs adopt Successive Interference Cancellation (SIC) to decode multiple received data from IoT devices to improve access efficiency. To enable an energy-sustainable capacity-optimal network, we study the joint problem of dynamic multi-UAV altitude control and multi-cell wireless channel access management of IoT devices as a stochastic control problem with multiple energy constraints. To learn an optimal control policy, we first formulate this problem as a Constrained Markov Decision Process (CMDP), and propose an online model-free Constrained Deep Reinforcement Learning (CDRL) algorithm based on Lagrangian primal-dual policy optimization to solve the CMDP. Extensive simulations demonstrate that our proposed algorithm learns a cooperative policy among UAVs in which the altitude of UAVs and channel access probability of IoT devices are dynamically and jointly controlled to attain the maximal long-term network capacity while maintaining energy sustainability of UAVs. The proposed algorithm outperforms Deep RL based solutions with reward shaping to account for energy costs, and achieves a temporal average system capacity which is $82.4\%$ higher than that of a feasible DRL based solution, and only $6.47\%$ lower compared to that of the energy-constraint-free system.

Includes members:
  Member "optimizing non orthogonal multiple access in random access networks":
    also connected through other hyperedges:
      (none)
  Member "deep reinforcement learning for minimizing age of information in uav assisted networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hybrid offline online design for uav enabled data harvesting in probabilistic los channel")
  Member "a game theoretic approach for noma aloha":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimizing non orthogonal multiple access in random access networks")
  Member "reward constrained policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploration exploitation in constrained mdps")
  Member "optimal 3d trajectory design and resource allocation for solar powered uav communication systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint trajectory and resource allocation design for energy efficient secure uav communication systems")
  Member "risk sensitive reinforcement learning a constrained optimization viewpoint":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving generalization of reinforcement learning with minimax distributional soft actor critic")
  Member "addressing function approximation error in actor critic methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "accelerated primal dual policy optimization for safe reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ipo interior point policy optimization under constraints")

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)