human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "deep learning based wireless resource allocation with application to vehicular networks"):
  with members:
    - "learning to branch accelerating resource allocation in wireless networks"
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "learning to detect"
    - "learning optimal resource allocations in wireless systems"
    - "deep learning based end to end wireless communication systems with conditional gan as unknown channel"
    - "distributed power control for large energy harvesting networks a multi agent deep reinforcement learning approach"
    - "power allocation in multi user cellular networks deep reinforcement learning approaches"
    - "toward intelligent vehicular networks a machine learning framework"

Connected through hyperedge HE-2 (induced by source paper "distributed learning for channel allocation over a shared spectrum"):
  with members:
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "game of thrones fully distributed learning for multi player bandits"
    - "deep multi user reinforcement learning for distributed dynamic spectrum access"
    - "game theoretic dynamic channel allocation for frequency selective interference channels"
    - "proactive resource management in lte u systems a deep learning perspective"
    - "propagation path loss models for 5g urban micro and macro cellular scenarios"
    - "multi player bandits a musical chairs approach"
    - "distributed channel assignment in cognitive radio networks stable matching and walrasian equilibrium"

Connected through hyperedge HE-3 (induced by source paper "data correlation aware resource management in wireless virtual reality vr an echo state transfer learning approach"):
  with members:
    - "cache placement in two tier hetnets with limited storage capacity cache or buffer"
    - "communication computing and caching for mobile vr delivery modeling and trade off"
    - "edge computing meets millimeter wave enabled vr paving the way to cutting the cord"
    - "human in the loop wireless communications machine learning and brain aware resource management"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"
    - "virtual reality over wireless networks quality of service model and learning based resource management"
    - "echo state transfer learning for data correlation aware resource allocation in wireless virtual reality"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"

Connected through hyperedge HE-4 (induced by source paper "non uniform time step deep q network for carrier sense multiple access in heterogeneous wireless networks"):
  with members:
    - "distributive dynamic spectrum access through deep reinforcement learning a reservoir computing based approach"
    - "carrier sense multiple access for heterogeneous wireless networks using deep reinforcement learning"
    - "actor critic deep reinforcement learning for dynamic multichannel access"
    - "application of machine learning in wireless networks key techniques and open issues"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep reinforcement learning an overview"
    - "deep multi user reinforcement learning for distributed dynamic spectrum access"

Connected through hyperedge HE-5 (induced by source paper "artificial intelligence enabled intelligent 6g networks"):
  with members:
    - "the roadmap to 6g ai empowered wireless networks"
    - "optimizing space air ground integrated networks by artificial intelligence"
    - "big data analytics machine learning and artificial intelligence in next generation wireless networks"
    - "what will 5g be"
    - "five disruptive technology directions for 5g"

Connected through hyperedge HE-6 (induced by source paper "machine learning for wireless connectivity and security of cellular connected uavs"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "mobility support for cellular connected unmanned aerial vehicles performance and analysis"
    - "securing uav communications via joint trajectory and power control"
    - "deep learning for signal authentication and security in massive internet of things systems"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"
    - "job selection in a network of autonomous uavs for delivery of goods"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"

Connected through hyperedge HE-7 (induced by source paper "carrier sense multiple access for heterogeneous wireless networks using deep reinforcement learning"):
  with members:
    - "distributive dynamic spectrum access through deep reinforcement learning a reservoir computing based approach"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep reinforcement learning an overview"
    - "deep reinforcement learning multiple access for heterogeneous wireless networks"
    - "deep residual learning for image recognition"

Question: Please tell me which class the center node 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)