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 in lte u systems a deep learning perspective"
  Abstract: LTE in unlicensed spectrum (LTE-U) is a promising approach to overcome the wireless spectrum scarcity. However, to reap the benefits of LTE-U, 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-U 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-U operators. Adopting a proactive coexistence mechanism enables future delay-intolerant LTE-U 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-U 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-U 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-U network.

Connected through hyperedge HE-1 (induced by source paper "pops policy pruning and shrinking for deep reinforcement learning"):
  with members:
    - "legr filter pruning via learned global ranking"
    - "structured pruning of neural networks with budget aware regularization"
    - "amc automl for model compression and acceleration on mobile devices"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "layer compensated pruning for resource constrained convolutional neural networks"
    - "deep learning in mobile and wireless networking a survey"
    - "deep reinforcement one shot learning for artificially intelligent classification systems"
    - "deep reinforcement learning an overview"

Connected through hyperedge HE-2 (induced by source paper "mac protocol design optimization using deep learning"):
  with members:
    - "unboxing mac protocol design optimization using deep learning"
    - "word2ket space efficient word embeddings inspired by quantum entanglement"
    - "internet congestion control via deep reinforcement learning"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep multi user reinforcement learning for distributed dynamic spectrum access"
    - "deep reinforcement learning multiple access for heterogeneous wireless networks"

Connected through hyperedge HE-3 (induced by source paper "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "game theory for multi access edge computing survey use cases and future trends"
    - "a deep q learning method for downlink power allocation in multi cell networks"
    - "machine learning in iot security current solutions and future challenges"
    - "toward intelligent vehicular networks a machine learning framework"
    - "distributed learning for channel allocation over a shared spectrum"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "deep learning for radio resource allocation in multi cell networks"

Connected through hyperedge HE-4 (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"
    - "proactive resource management for lte in unlicensed spectrum a deep learning perspective"
    - "deep multi user reinforcement learning for distributed dynamic spectrum access"
    - "game theoretic dynamic channel allocation for frequency selective interference channels"
    - "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-5 (induced by source paper "a novel channel reservation mechanism for next generation cellular networks"):
  with members:
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "towards the 5th generation of wireless communication systems"
    - "power and channel allocation for non orthogonal multiple access in 5g systems tractability and computation"

Connected through hyperedge HE-6 (induced by source paper "contract based incentive mechanism for lte over unlicensed channels"):
  with members:
    - "orla olaa orthogonal coexistence of laa and wifi in unlicensed spectrum"
    - "echo state networks for self organizing resource allocation in lte u with uplink downlink decoupling"
    - "network formation in the sky unmanned aerial vehicles for multi hop wireless backhauling"
    - "the impact of unlicensed access on small cell resource allocation"
    - "breaking the economic barrier of caching in cellular networks incentives and contracts"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"
    - "auction based coopetition between lte unlicensed and wi fi"
    - "joint caching and pricing strategies for popular content in information centric networks"

Connected through hyperedge HE-7 (induced by source paper "deep learning for signal authentication and security in massive internet of things systems"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "fast uplink grant for machine type communications challenges and opportunities"
    - "deep learning for reliable mobile edge analytics in intelligent transportation systems"
    - "deep learning based dynamic watermarking for secure signal authentication in the internet of things"
    - "cognitive hierarchy theory for distributed resource allocation in the internet of things"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "colonel blotto game for secure state estimation in interdependent critical infrastructure"
    - "deep reinforcement learning from self play in imperfect information games"

Connected through hyperedge HE-8 (induced by source paper "deep learning for radio resource allocation in multi cell networks"):
  with members:
    - "learning optimal resource allocations in wireless systems"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "deep reinforcement learning for resource allocation in v2v communications"

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)