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: "dirty paper coding for the mimo cognitive radio channel with imperfect csit"
  Abstract: A Dirty Paper Coding (DPC) based transmission scheme for the Gaussian multiple-input multiple-output (MIMO) cognitive radio channel (CRC) is studied when there is imperfect and perfect channel knowledge at the transmitters (CSIT) and the receivers, respectively. In particular, the problem of optimizing the sum-rate of the MIMO CRC over the transmit covariance matrices is dealt with. Such an optimization, under the DPC-based transmission strategy, needs to be performed jointly with an optimization over the inflation factor. To this end, first the problem of determination of inflation factor over the MIMO channel $Y=H_1 X + H_2 S + Z$ with imperfect CSIT is investigated. For this problem, two iterative algorithms, which generalize the corresponding algorithms proposed for the channel $Y=H(X+S)+Z$, are developed. Later, the necessary conditions for maximizing the sum-rate of the MIMO CRC over the transmit covariances for a given choice of inflation factor are derived. Using these necessary conditions and the algorithms for the determination of the inflation factor, an iterative, numerical algorithm for the joint optimization is proposed. Some interesting observations are made from the numerical results obtained from the algorithm. Furthermore, the high-SNR sum-rate scaling factor achievable over the CRC with imperfect CSIT is obtained.

Connected through hyperedge HE-1 (induced by source paper "prescient precoding in heterogeneous dsa networks with both underlay and interweave mimo cognitive radios"):
  with members:
    - "beamforming and rate allocation in miso cognitive radio networks"
    - "cognitive radio with partial channel state information at the transmitter"
    - "cooperative multi cell block diagonalization with per base station power constraints"
    - "exploiting multi antennas for opportunistic spectrum sharing in cognitive radio networks"
    - "spectrum sensing algorithms for cognitive radio based on statistical covariances"
    - "on the fading paper achievable region of the fading mimo broadcast channel"
    - "cognitive radio an information theoretic perspective"

Connected through hyperedge HE-2 (induced by source paper "cognitive radio with partial channel state information at the transmitter"):
  with members:
    - "on the achievable rate of the fading dirty paper channel with imperfect csit"
    - "writing on dirty paper with resizing and its application to quasi static fading broadcast channels"
    - "on the fading paper achievable region of the fading mimo broadcast channel"
    - "cognitive radio an information theoretic perspective"

Connected through hyperedge HE-3 (induced by source paper "the degrees of freedom regions of mimo broadcast interference and cognitive radio channels with no csit"):
  with members:
    - "real interference alignment exploiting the potential of single antenna systems"
    - "completely stale transmitter channel state information is still very useful"
    - "exploiting channel correlations simple interference alignment schemes with no csit"
    - "the degrees of freedom region of the mimo cognitive interference channel with no csit"
    - "the degrees of freedom of mimo interference channels without state information at transmitters"
    - "capacity bounds for two hop interference networks"
    - "interference alignment under limited feedback for mimo interference channels"
    - "isotropic mimo interference channels without csit the loss of degrees of freedom"

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)