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 "management and orchestration of virtual network functions via deep reinforcement learning"):
  Abstract: Management and orchestration (MANO) of resources by virtual network functions (VNFs) represents one of the key challenges towards a fully virtualized network architecture as envisaged by 5G standards. Current threshold-based policies inefficiently over-provision network resources and under-utilize available hardware, incurring high cost for network operators, and consequently, the users. In this work, we present a MANO algorithm for VNFs allowing a central unit (CU) to learn to autonomously re-configure resources (processing power and storage), deploy new VNF instances, or offload them to the cloud, depending on the network conditions, available pool of resources, and the VNF requirements, with the goal of minimizing a cost function that takes into account the economical cost as well as latency and the quality-of-service (QoS) experienced by the users. First, we formulate the stochastic resource optimization problem as a parameterized action Markov decision process (PAMDP). Then, we propose a solution based on deep reinforcement learning (DRL). More precisely, we present a novel RL approach called, parameterized action twin (PAT) deterministic policy gradient, which leverages an actor-critic architecture to learn to provision resources to the VNFs in an online manner. Finally, we present numerical performance results, and map them to 5G key performance indicators (KPIs). To the best of our knowledge, this is the first work that considers DRL for MANO of VNFs' physical resources.

Includes members:
  Member "deep reinforcement learning with discrete normalized advantage functions for resource management in network slicing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gan based deep distributional reinforcement learning for resource management in network slicing")
  Member "machine learning in the air":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  Member "deep reinforcement learning for resource management in network slicing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimizing throughput performance in distributed mimo wi fi networks using deep reinforcement learning")
  Member "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep reinforcement learning for backscatter aided data offloading in mobile edge computing")
  Member "multi tenant cross slice resource orchestration a deep reinforcement learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to branch accelerating resource allocation in wireless networks")
  Member "auto scaling network resources using machine learning to improve qos and reduce cost":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "blockchain assisted spectrum trading between elastic virtual optical networks")
  Member "addressing function approximation error in actor critic methods":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "deep learning in mobile and wireless networking a survey":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning")

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)