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 "proactive resource management for lte in unlicensed spectrum a deep learning perspective"):
  Abstract: LTE in unlicensed spectrum using licensed assisted access LTE (LTE-LAA) is a promising approach to overcome the wireless spectrum scarcity. However, to reap the benefits of LTE-LAA, a fair coexistence mechanism with other incumbent WiFi deployments is required. In this paper, a novel deep learning approach is proposed for modeling the resource allocation problem of LTE-LAA small base stations (SBSs). The proposed approach enables multiple SBSs to proactively perform dynamic channel selection, carrier aggregation, and fractional spectrum access while guaranteeing fairness with existing WiFi networks and other LTE-LAA operators. Adopting a proactive coexistence mechanism enables future delay-tolerant LTE-LAA data demands to be served within a given prediction window ahead of their actual arrival time thus avoiding the underutilization of the unlicensed spectrum during off-peak hours while maximizing the total served LTE-LAA traffic load. To this end, a noncooperative game model is formulated in which SBSs are modeled as Homo Egualis agents that aim at predicting a sequence of future actions and thus achieving long-term equal weighted fairness with WLAN and other LTE-LAA operators over a given time horizon. The proposed deep learning algorithm is then shown to reach a mixed-strategy Nash equilibrium (NE), when it converges. Simulation results using real data traces show that the proposed scheme can yield up to 28% and 11% gains over a conventional reactive approach and a proportional fair coexistence mechanism, respectively. The results also show that the proposed framework prevents WiFi performance degradation for a densely deployed LTE-LAA network.

Includes members:
  Member "deep learning for reliable mobile edge analytics in intelligent transportation systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal time allocation in vanets advertising a price based approach using stackelberg game")
  Member "echo state networks for self organizing resource allocation in lte u with uplink downlink decoupling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "20 years of evolution from cognitive to intelligent communications")
  Member "lstm a search space odyssey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "inter operator resource management for millimeter wave multi hop backhaul networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "max min fair resource allocation in millimetre wave backhauls")
  Member "deep learning based dynamic watermarking for secure signal authentication in the internet of things":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks for distributed intrusion detection in the internet of things")
  Member "holistic small cell traffic balancing across licensed and unlicensed bands":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "echo state networks for self organizing resource allocation in lte u with uplink downlink decoupling")
  Member "coordinated dynamic spectrum management of lte u and wi fi networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ofdmfi enabling cross technology communication between lte u laa and wifi")
  Member "learning to cooperate via policy search":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication")

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)