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: "beamforming and rate allocation in miso cognitive radio networks"
  Abstract: We consider decentralized multiantenna cognitive radio networks where the secondary (cognitive) users are granted simultaneous spectrum access along with the license-holding (primary) users. We treat the problem of distributed beamforming and rate allocation for the secondary users such that the minimum weighted secondary rate is maximized. Such an optimization is subject to (1) a limited weighted sum-power budget for the secondary users and (2) guaranteed protection for the primary users in the sense that the interference level imposed on each primary receiver does not exceed a specified level. Based on the decoding method deployed by the secondary receivers, we consider three scenarios for solving this problem. In the first scenario, each secondary receiver decodes only its designated transmitter while suppressing the rest as Gaussian interferers (single-user decoding). In the second case, each secondary receiver employs the maximum likelihood decoder (MLD) to jointly decode all secondary transmissions. In the third one, each secondary receiver uses the unconstrained group decoder (UGD). By deploying the UGD, each secondary user is allowed to decode any arbitrary subset of users (which contains its designated user) after suppressing or canceling the remaining users. We offer an optimal distributed algorithm for designing the beamformers and allocating rates in the first scenario (i.e., with single-user decoding). We also provide explicit formulations of the optimization problems for the latter two scenarios (with the MLD and the UGD, respectively), which, however are nonconvex. While we provide a suboptimal centralized algorithm for the case with MLD, neither of the two scenarios can be solved efficiently in a decentralized setup. As a remedy, we offer two-stage suboptimal distributed algorithms for solving the problem for the MLD and UGD scenarios. In the first stage, the beamformers and rates are determined in a distributed fashion after assuming single user decoding at each secondary receiver. By using these beamformer designs, MLD often and UGD always allow for supporting rates higher than those achieved in the first stage. Based on this observation, we construct the second stage by offering optimal distributed low-complexity algorithms to allocate excess rates to the secondary users such that a notion of fairness is maintained. Analytical and empirical results demonstrate the gains yielded by the proposed rate allocation and the beamformer design algorithms.

Connected through hyperedge HE-1 (induced by source paper "an efficient precoder design for multiuser mimo cognitive radio networks with interference constraints"):
  with members:
    - "collaborative relay beamforming for secrecy"
    - "cooperative multi cell block diagonalization with per base station power constraints"
    - "exploiting multi antennas for opportunistic spectrum sharing in cognitive radio networks"
    - "on peak versus average interference power constraints for protecting primary users in cognitive radio networks"
    - "weighted sum rate optimization for cognitive radio mimo broadcast channels"
    - "capacity limits of cognitive radio with distributed and dynamic spectral activity"

Connected through hyperedge HE-2 (induced by source paper "robust resource allocation for full duplex cognitive radio systems"):
  with members:
    - "robust beamforming for swipt systems with non linear energy harvesting model"
    - "robust layered transmission in secure miso multiuser unicast cognitive radio systems"
    - "power efficient resource allocation for full duplex radio distributed antenna networks"
    - "multi objective optimization for robust power efficient and secure full duplex wireless communication systems"
    - "multipair full duplex relaying with massive arrays and linear processing"
    - "on the spectral efficiency of full duplex small cell wireless systems"
    - "full duplex cooperative cognitive radio with transmit imperfections"

Connected through hyperedge HE-3 (induced by source paper "cooperative cognitive relaying under primary and secondary quality of service satisfaction"):
  with members:
    - "exploiting multi antennas for opportunistic spectrum sharing in cognitive radio networks"
    - "cognitive beamforming made practical effective interference channel and learning throughput tradeoff"
    - "collaborative beamforming for distributed wireless ad hoc sensor networks"

Connected through hyperedge HE-4 (induced by source paper "mimo precoding in underlay cognitive radio systems with completely unknown primary csi"):
  with members:
    - "rank constrained schur convex optimization with multiple trace log det constraints"
    - "interference alignment as a rank constrained rank minimization"
    - "optimal spectrum sharing in mimo cognitive radio networks via semidefinite programming"
    - "exploiting multi antennas for opportunistic spectrum sharing in cognitive radio networks"
    - "on the capacity of doubly correlated mimo channels"

Connected through hyperedge HE-5 (induced by source paper "prescient precoding in heterogeneous dsa networks with both underlay and interweave mimo cognitive radios"):
  with members:
    - "cognitive radio with partial channel state information at the transmitter"
    - "dirty paper coding for the mimo cognitive radio channel with imperfect csit"
    - "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-6 (induced by source paper "dynamic resource allocation in cognitive radio networks"):
  with members:
    - "on the relationship between the multi antenna secrecy communications and cognitive radio communications"
    - "on ergodic sum capacity of fading cognitive multiple access and broadcast channels"
    - "iterative spectrum shaping with opportunistic multiuser detection"
    - "on gaussian mimo bc mac duality with multiple transmit covariance constraints"
    - "exploiting multi antennas for opportunistic spectrum sharing in cognitive radio networks"
    - "competitive design of multiuser mimo systems based on game theory a unified view"
    - "cognitive mimo radio"
    - "cognitive beamforming made practical effective interference channel and learning throughput tradeoff"

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)