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 "echo liquid state deep learning for 360 circ content transmission and caching in wireless vr networks with cellular connected uavs"):
  Abstract: In this paper, the problem of content caching and transmission is studied for a wireless virtual reality (VR) network in which unmanned aerial vehicles (UAVs) capture videos on live games or sceneries and transmit them to small base stations (SBSs) that service the VR users. However, due to its limited capacity, the wireless network may not be able to meet the delay requirements of such 360 content transmissions. To meet the VR delay requirements, the UAVs can extract specific visible content (e.g., user field of view) from the original 360 data and send this visible content to the users so as to reduce the traffic load over backhaul and radio access links. To further alleviate the UAV-SBS backhaul traffic, the SBSs can also cache the popular contents that users request. This joint content caching and transmission problem is formulated as an optimization problem whose goal is to maximize the users' reliability, defined as the probability that the content transmission delay of each user satisfies the instantaneous VR delay target. To address this problem, a distributed deep learning algorithm that brings together new neural network ideas from liquid state machine (LSM) and echo state networks (ESNs) is proposed. The proposed algorithm enables each SBS to predict the users' reliability so as to find the optimal contents to cache and content transmission format for each UAV. Analytical results are derived to expose the various network factors that impact content caching and content transmission format selection. Simulation results show that the proposed algorithm yields 25.4% gain of reliability compared to Q-learning. The results also show that the proposed algorithm can achieve 14.7% gain of reliability due to the reduction of traffic load over backhaul compared to the proposed algorithm with random caching.

Includes members:
  Member "echo state networks for self organizing resource allocation in lte u with uplink downlink decoupling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "20 years of evolution from cognitive to intelligent communications")
  Member "caching meets millimeter wave communications for enhanced mobility management in 5g networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a tutorial on beam management for 3gpp nr at mmwave frequencies")
  Member "the 5g cellular backhaul management dilemma to cache or to serve":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "security of 5g mobile backhaul networks a survey")
  Member "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the age of information in internet of things systems with correlated devices")
  Member "cluster content caching an energy efficient approach to improve quality of service in cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "data correlation aware resource management in wireless virtual reality vr an echo state transfer learning approach")
  Member "joint caching and pricing strategies for popular content in information centric networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cost sharing games for energy efficient multi hop broadcast in wireless networks")
  Member "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")

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)