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: "are we approaching the fundamental limits of wireless network densification"
  Abstract: The single most important factor behind the data rate increases experienced by users of wireless networks over the past few decades has been densification, namely adding more base stations and access points and thus getting more spatial reuse of the spectrum. This trend is set to continue into 5G and presumably beyond. However, at some point further densification will no longer be able to provide exponentially increasing data rates. Like the end of Moore's Law, this would have massive implications on the entire technology landscape, which depends ever more heavily on wireless connectivity. When and why will this happen? How might we prolong this from occurring for as long as possible? These are the questions explored in this paper.

Connected through hyperedge HE-1 (induced by source paper "resource management in wireless networks via multi agent deep reinforcement learning"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "dota 2 with large scale deep reinforcement learning"
    - "on the power of curriculum learning in training deep networks"
    - "a deep q learning method for downlink power allocation in multi cell networks"
    - "deep reinforcement learning for distributed uncoordinated cognitive radios resource allocation"
    - "zpd teaching strategies for deep reinforcement learning from demonstrations"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "thirty years of machine learning the road to pareto optimal next generation wireless networks"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "an introduction to deep learning for the physical layer"
    - "budget constrained multi armed bandits with multiple plays"
    - "unsupervised machine learning for networking techniques applications and research challenges"
    - "a survey of multi objective optimization in wireless sensor networks metrics algorithms and open problems"
    - "recent trends in deep learning based natural language processing"

Connected through hyperedge HE-3 (induced by source paper "smart radio environments empowered by ai reconfigurable meta surfaces an idea whose time has come"):
  with members:
    - "a novel communication paradigm for high capacity and security via programmable indoor wireless environments in next generation wireless systems"
    - "reflection probability in wireless networks with metasurface coated environmental objects an approach based on random spatial processes"
    - "a tractable analysis of the blind spot probability in localization networks under correlated blocking"
    - "using any surface to realize a new paradigm for wireless communications"
    - "wireless network intelligence at the edge"
    - "medium access control in wireless network on chip a context analysis"
    - "beyond massive mimo the potential of positioning with large intelligent surfaces"
    - "realizing wireless communication through software defined hypersurface environments"

Connected through hyperedge HE-4 (induced by source paper "towards smart and reconfigurable environment intelligent reflecting surface aided wireless network"):
  with members:
    - "beamforming optimization for wireless network aided by intelligent reflecting surface with discrete phase shifts"
    - "weighted sum power maximization for intelligent reflecting surface aided swipt"
    - "intelligent reflecting surface assisted secrecy communication via joint beamforming and jamming"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "achievable rate maximization by passive intelligent mirrors"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "spectral and energy efficient wireless powered iot networks noma or tdma"
    - "beyond massive mimo the potential of data transmission with large intelligent surfaces"

Connected through hyperedge HE-5 (induced by source paper "ubiquitous cell free massive mimo communications"):
  with members:
    - "downlink training in cell free massive mimo a blessing in disguise"
    - "scalability aspects of cell free massive mimo"
    - "making cell free massive mimo competitive with mmse processing and centralized implementation"
    - "fog massive mimo a user centric seamless hot spot architecture"
    - "enhancing favorable propagation in cell free massive mimo through spatial user grouping"
    - "on the total energy efficiency of cell free massive mimo"
    - "channel hardening and favorable propagation in cell free massive mimo with stochastic geometry"
    - "cell free massive mimo versus small cells"

Connected through hyperedge HE-6 (induced by source paper "local partial zero forcing precoding for cell free massive mimo"):
  with members:
    - "scalability aspects of cell free massive mimo"
    - "ubiquitous cell free massive mimo communications"
    - "making cell free massive mimo competitive with mmse processing and centralized implementation"
    - "downlink spectral efficiency of cell free massive mimo with full pilot zero forcing"
    - "cell free massive mimo with limited backhaul"
    - "on the total energy efficiency of cell free massive mimo"
    - "cell free massive mimo versus small cells"
    - "reciprocity calibration for massive mimo proposal modeling and validation"

Connected through hyperedge HE-7 (induced by source paper "spectral efficiency analysis in dense massive mimo networks"):
  with members:
    - "network deployment for maximal energy efficiency in uplink with multislope path loss"
    - "massive mimo has unlimited capacity"
    - "performance impact of base station antenna heights in dense cellular networks"
    - "impact of los nlos propagation and path loss in ultra dense cellular networks"
    - "deploying dense networks for maximal energy efficiency small cells meet massive mimo"
    - "5g ultra dense cellular networks"
    - "downlink cellular network analysis with multi slope path loss models"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-8 (induced by source paper "on the coverage and capacity of ultra dense networks with directional transmissions"):
  with members:
    - "a unified asymptotic analysis of area spectral efficiency in ultradense cellular networks"
    - "downlink cellular network analysis with los nlos propagation and elevated base stations"
    - "performance analysis of millimeter wave relaying impacts of beamwidth and self interference"
    - "downlink cellular network analysis with multi slope path loss models"
    - "coverage and rate analysis for millimeter wave cellular networks"
    - "what will 5g be"
    - "a tractable approach to coverage and rate in cellular networks"

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)