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: "the capacity of mimo channels with per antenna power constraint"
  Abstract: We establish the optimal input signaling and the capacity of MIMO channels under per-antenna power constraint. While admitting a linear eigenbeam structure, the optimal input is no longer diagonalizable by the channel right singular vectors as with sum power constraint. We formulate the capacity optimization as an SDP problem and solve in closed-form the optimal input covariance as a function of the dual variable. We then design an efficient algorithm to find this optimal input signaling for all channel sizes. The proposed algorithm allows for straightforward implementation in practical systems in real time. Simulation results show that with equal constraint per antenna, capacity with per-antenna power can be close to capacity with sum power, but as the constraint becomes more skew, the two capacities diverge. Forcing input eigenbeams to match the channel right singular vectors achieves no improvement over independent signaling and can even be detrimental to capacity.

Connected through hyperedge HE-1 (induced by source paper "mimo capacity with average total and per antenna power constraints"):
  with members:
    - "the capacity of gaussian mimo channels under total and per antenna power constraints"
    - "miso capacity with per antenna power constraint"

Connected through hyperedge HE-2 (induced by source paper "alternating optimization for capacity region of gaussian mimo broadcast channels with per antenna power constraint"):
  with members:
    - "miso capacity with per antenna power constraint"

Connected through hyperedge HE-3 (induced by source paper "low complexity approaches for mimo capacity with per antenna power constraint"):
  with members:
    - "miso capacity with per antenna power constraint"

Connected through hyperedge HE-4 (induced by source paper "signaling design of two way mimo full duplex channel optimality under imperfect transmit front end chain"):
  with members:
    - "energy efficient power control a look at 5g wireless technologies"
    - "hardware impairments aware transceiver for full duplex massive mimo relaying"
    - "optimal signaling of miso full duplex two way wireless channel"
    - "coalitional games for transmitter cooperation in mimo multiple access channels"
    - "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"
    - "understanding the impact of phase noise on active cancellation in wireless full duplex"
    - "experiment driven characterization of full duplex wireless systems"
    - "optimal beamforming in interference networks with perfect local channel information"

Connected through hyperedge HE-5 (induced by source paper "coalitional games for transmitter cooperation in mimo multiple access channels"):
  with members:
    - "distributed learning policies for power allocation in multiple access channels"
    - "power allocation games in wireless networks of multi antenna terminals"
    - "dynamic power allocation games in parallel multiple access channels"
    - "methodologies for analyzing equilibria in wireless games"
    - "coalitions in cooperative wireless networks"
    - "cores of cooperative games in information theory"

Connected through hyperedge HE-6 (induced by source paper "iterative mode dropping for the sum capacity of mimo mac with per antenna power constraint"):
  with members:
    - "miso capacity with per antenna power constraint"

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)