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: "adaptive training for correlated fading channels with feedback"
  Abstract: We consider data transmission through a time-selective, correlated (first-order Markov) Rayleigh fading channel subject to an average power constraint. The channel is estimated at the receiver with a pilot signal, and the estimate is fed back to the transmitter. The estimate is used for coherent demodulation, and to adapt the data and pilot powers. We derive the Hamilton--Jacobi--Bellman (HJB) equation for the optimal policy in a continuous-time limit where the channel state evolves as an Ornstein--Uhlenbeck diffusion process, and is estimated by a Kalman filter at the receiver. Finding an explicit solution to the HJB equation, as well as proving that a (twice-differentiable) solution exists, appears to be quite challenging. However, assuming that such a solution does exist, we explicitly determine the optimal pilot and data power control policies. The optimal pilot policy switches between zero and the maximum (peak-constrained) value (“bang-bang” control), and approximates the optimal discrete-time policy at low signal-to-noise ratios (SNRs) (equivalently, large bandwidths). The switching boundary is defined in terms of the system state (estimated channel mean and associated error variance), and can be explicitly computed. Under the optimal policy, the transmitter conserves power by decreasing the training power when the channel is faded, thereby increasing the data rate. Numerical results show a significant increase in achievable rate due to the adaptive training scheme with feedback, relative to constant (nonadaptive) training, which does not require feedback. The gain is more pronounced at relatively low SNRs and with fast fading. Results are further verified through Monte Carlo simulations.

Connected through hyperedge HE-1 (induced by source paper "spatio temporal network dynamics framework for energy efficient ultra dense cellular networks"):
  with members:
    - "tractable resource management with uplink decoupled millimeter wave overlay in ultra dense cellular networks"
    - "energy efficient resource management in ultra dense small cell networks a mean field approach"
    - "on coordinating ultra dense wireless access networks optimization modeling algorithms and insights"
    - "five disruptive technology directions for 5g"
    - "analysis of blockage effects on urban cellular networks"
    - "coverage and economy of cellular networks with many base stations"
    - "downlink capacity and base station density in cellular networks"
    - "a tractable approach to coverage and rate in cellular networks"

Connected through hyperedge HE-2 (induced by source paper "user centric mobility management in ultra dense cellular networks under spatio temporal dynamics"):
  with members:
    - "tractable resource management with uplink decoupled millimeter wave overlay in ultra dense cellular networks"
    - "towards 1 gbps ue in cellular systems understanding ultra dense small cell deployments"
    - "tractable resource management in millimeter wave overlaid ultra dense cellular networks"
    - "stochastic differential games and energy efficient power control"

Connected through hyperedge HE-3 (induced by source paper "impact of residual transmit rf impairments on training based mimo systems"):
  with members:
    - "pilot beam pattern design for channel estimation in massive mimo systems"
    - "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"
    - "on the mimo capacity with residual transceiver hardware impairments"
    - "capacity bounds for mimo microwave backhaul links affected by phase noise"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "on the impact of hardware impairments on massive mimo"
    - "two way relaying under the presence of relay transceiver hardware impairments"
    - "capacity limits and multiplexing gains of mimo channels with transceiver impairments"

Connected through hyperedge HE-4 (induced by source paper "optimal training for non feedback adaptive psam over time varying rayleigh fading channels"):
  with members:
    - "fundamental limits of cooperation"
    - "training optimization for gauss markov rayleigh fading channels"

Connected through hyperedge HE-5 (induced by source paper "stochastic differential games and energy efficient power control"):
  with members:
    - "energy efficient precoding for multiple antenna terminals"
    - "a stochastic game formulation of energy efficient power control equilibrium utilities and practical strategies"
    - "long term energy constraints and power control in cognitive radio networks"
    - "power allocation games for mimo multiple access channels with coordination"
    - "introducing hierarchy in energy games"
    - "non atomic games for multi user systems"

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)