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: "how dense is ultra dense for wireless networks from far to near field communications"
  Abstract: Besides advanced telecommunications techniques, the most prominent evolution of wireless networks is the densification of network deployment. In particular, the increasing access points/users density and reduced cell size significantly enhance spatial reuse, thereby improving network capacity. Nevertheless, does network ultra-densification and over-deployment always boost the performance of wireless networks? Since the distance from transmitters to receivers is greatly reduced in dense networks, signal is more likely to be propagated from far- to near-field region. Without considering near-field propagation features, conventional understandings of the impact of network densification become doubtful. With this regard, it is imperative to reconsider the pros and cons brought by network densification. In this article, we first discuss the near-field propagation features in densely deployed network and verify through experimental results the validity of the proposed near-field propagation model. Considering near-field propagation, we further explore how dense is ultra-dense for wireless networks and provide a concrete interpretation of ultra-densification from the spatial throughput perspective. Meanwhile, as near-field propagation makes interference more complicated and difficult to handle, we shed light on the key challenges of applying interference management in ultra-dense wireless networks. Moreover, possible solutions are presented to suggest future directions.

Connected through hyperedge HE-1 (induced by source paper "a brief history on the theoretical analysis of dense small cell wireless networks"):
  with members:
    - "optimal base station antenna downtilt in downlink cellular networks"
    - "sinr and throughput of dense cellular networks with stretched exponential path loss"
    - "ultra dense networks is there a limit to spatial spectrum reuse"
    - "wireless powered dense cellular networks how many small cells do we need"
    - "inter operator base station coordination in spectrum shared millimeter wave cellular networks"
    - "downlink cellular network analysis with los nlos propagation and elevated base stations"
    - "performance impact of base station antenna heights in dense cellular networks"
    - "modeling infrastructure sharing in mmwave networks with shared spectrum licenses"

Connected through hyperedge HE-2 (induced by source paper "performance limits of network densification"):
  with members:
    - "coverage and capacity scaling laws in downlink ultra dense cellular networks"
    - "impact of los nlos propagation and path loss in ultra dense cellular networks"
    - "performance impact of los and nlos transmissions in dense cellular networks"
    - "a unified fading model using infinitely divisible distributions"
    - "towards 1 gbps ue in cellular systems understanding ultra dense small cell deployments"
    - "spectral efficiency scaling laws in dense random wireless networks with multiple receive antennas"
    - "downlink cellular network analysis with multi slope path loss models"
    - "modeling and analysis of k tier downlink heterogeneous 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)