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: "sum rate maximization in two way af mimo relaying polynomial time solutions to a class of dc programming problems"
  Abstract: Sum-rate maximization in two-way amplify-and-forward (AF) multiple-input multiple-output (MIMO) relaying belongs to the class of difference-of-convex functions (DC) programming problems. DC programming problems occur also in other signal processing applications and are typically solved using different modifications of the branch-and-bound method which, however, does not have any polynomial time complexity guarantees. In this paper, we develop two efficient polynomial time algorithms for the sum-rate maximization in two-way AF MIMO relaying. The first algorithm guarantees to find at least a Karush-Kuhn-Tucker (KKT) solution. There is a strong evidence, however, that such a solution is actually globally optimal. The second algorithm that is based on the generalized eigenvectors shows the same performance as the first one with reduced computational complexity. The objective function of the problem is represented as a product of quadratic fractional ratios and parameterized so that its convex part (versus the concave part) contains only one (or two) optimization variables. One of the algorithms is called POlynomial Time DC (POTDC) and is based on semi-definite programming (SDP) relaxation, linearization, and an iterative Newton-type search over a single parameter. The other algorithm is called RAte-maximization via Generalized EigenvectorS (RAGES) and is based on the generalized eigenvectors method and an iterative search over two (or one, in its approximate version) optimization variables. We derive an upper-bound for the optimal value of the corresponding optimization problem and show by simulations that this upper-bound is achieved by both algorithms. It provides an evidence that the algorithms find a global optimum. The proposed methods are also superior to other state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "joint beamforming and power allocation in downlink noma multiuser mimo networks"):
  with members:
    - "joint beamforming and power splitting control in downlink cooperative swipt noma systems"
    - "noma in 5g systems exciting possibilities for enhancing spectral efficiency"
    - "capacity comparison between mimo noma and mimo oma with multiple users in a cluster"
    - "robust resource allocation for mimo wireless powered communication networks based on a non linear eh model"
    - "design of massive mimo noma with limited feedback"
    - "on the spectral efficiency and security enhancements of noma assisted multicast unicast streaming"
    - "a minorization maximization method for optimizing sum rate in the downlink of non orthogonal multiple access systems"
    - "non orthogonal multiple access noma for downlink multiuser mimo systems user clustering beamforming and power allocation"

Connected through hyperedge HE-2 (induced by source paper "fast algorithms for designing unimodular waveform s with good correlation properties"):
  with members:
    - "ambiguity function of the transmit beamspace based mimo radar"
    - "phased mimo radar a tradeoff between phased array and mimo radars"

Connected through hyperedge HE-3 (induced by source paper "joint power allocation and beamforming for energy efficient two way multi relay communications"):
  with members:
    - "energy efficient power control a look at 5g wireless technologies"
    - "a survey of energy efficient techniques for 5g networks and challenges ahead"
    - "a survey on device to device communication in cellular networks"
    - "multiobjective signal processing optimization the way to balance conflicting metrics in 5g systems"
    - "linear precoding designs for amplify and forward multiuser two way relay systems"
    - "on design of collaborative beamforming for two way relay networks"
    - "optimal beamforming for two way multi antenna relay channel with analogue network coding"

Connected through hyperedge HE-4 (induced by source paper "parallel and distributed methods for nonconvex optimization part ii applications"):
  with members:
    - "parallel selective algorithms for big data optimization"
    - "parallel and distributed methods for nonconvex optimization part i theory"
    - "decomposition by partial linearization parallel optimization of multi agent systems"
    - "weighted fair multicast multigroup beamforming under per antenna power constraints"
    - "alternating rate profile optimization in single stream mimo interference channels"
    - "coordinated multicast beamforming in multicell networks"
    - "distributed optimal beamformers for cognitive radios robust to channel uncertainties"
    - "achieving global optimality for weighted sum rate maximization in the k user gaussian interference channel with multiple antennas"

Connected through hyperedge HE-5 (induced by source paper "rank two beamforming and power allocation in multicasting relay networks"):
  with members:
    - "physical layer multicasting by stochastic transmit beamforming and alamouti space time coding"
    - "network beamforming using relays with perfect channel information"

Connected through hyperedge HE-6 (induced by source paper "on the optimal precoding for mimo gaussian wire tap channels"):
  with members:
    - "towards the secrecy capacity of the gaussian mimo wire tap channel the 2 2 1 channel"
    - "the secrecy capacity of the mimo wiretap channel"

Connected through hyperedge HE-7 (induced by source paper "sum rate maximization with minimum power consumption for mimo df two way relaying part i relay optimization"):
  with members:
    - "joint source and relay precoding designs for mimo two way relaying based on mse criterion"
    - "power allocation based on sep minimization in two hop decode and forward relay networks"

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)