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: "modeling infrastructure sharing in mmwave networks with shared spectrum licenses"
  Abstract: Competing cellular operators aggressively share infrastructure in many major US markets. If operators also were to share spectrum in next-generation millimeter-wave (mmWave) networks, intra-cellular interference will become correlated with inter-cellular interference. We propose a mathematical framework to model a multi-operator mmWave cellular network with co-located base-stations (BSs). We then characterize the signal-to-interference-plus-noise ratio (SINR) distribution for an arbitrary network and derive its coverage probability. To understand how varying the spatial correlation between different networks affects coverage probability, we derive special results for the two-operator scenario, where we construct the operators' individual networks from a single network via probabilistic coupling. For external validation, we devise a method to quantify and estimate spatial correlation from actual base-station deployments. We compare our two-operator model against an actual macro-cell-dominated network and an actual network primarily comprising distributed-antenna-system (DAS) nodes. Using the actual deployment data to set the parameters of our model, we observe that coverage probabilities for the model and actual deployments not only compare very well to each other, but also match nearly perfectly for the case of the DAS-node-dominated deployment. Another interesting observation is that a network that shares spectrum and infrastructure has a lower rate coverage probability than a network of the same number of BSs that shares neither spectrum nor infrastructure, suggesting that the latter is more suitable for low-rate applications.

Connected through hyperedge HE-1 (induced by source paper "a hybrid model based and data driven approach to spectrum sharing in mmwave cellular networks"):
  with members:
    - "how to escape saddle points efficiently"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "subspace estimation and decomposition for large millimeter wave mimo systems"
    - "spectrum sharing in mmwave cellular networks via cell association coordination and beamforming"
    - "sum rate maximization in sub 28 ghz millimeter wave mimo interfering networks"
    - "stochastic geometry modeling and analysis of multi tier millimeter wave cellular networks"
    - "the transitional behavior of interference in millimeter wave networks and its impact on medium access control"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (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"
    - "spatially correlated content caching for device to device 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)