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: "an efficient global algorithm for single group multicast beamforming"
  Abstract: Consider the single-group multicast beamforming problem, where multiple users receive the same data stream simultaneously from a single transmitter. The problem is NP-hard and all existing algorithms for the problem either find suboptimal approximate or local stationary solutions. In this paper, we propose an efficient branch-and-bound algorithm for the problem that is guaranteed to find its global solution. To the best of our knowledge, our proposed algorithm is the first tailored global algorithm for the single-group multicast beamforming problem. Simulation results show that our proposed algorithm is computationally efficient (albeit its theoretical worst-case iteration complexity is exponential with respect to the number of receivers) and it significantly outperforms a state-of-the-art general-purpose global optimization solver called Baron. Our proposed algorithm provides an important benchmark for performance evaluation of existing algorithms for the same problem. By using it as the benchmark, we show that two state-of-the-art algorithms, semidefinite relaxation algorithm and successive linear approximation algorithm, work well when the problem dimension (i.e., the number of antennas at the transmitter and the number of receivers) is small but their performance deteriorates quickly as the problem dimension increases.

Connected through hyperedge HE-1 (induced by source paper "federated learning via over the air computation"):
  with members:
    - "towards federated learning at scale system design"
    - "federated machine learning concept and applications"
    - "joint activity detection and channel estimation for iot networks phase transition and computation estimation tradeoff"
    - "wireless network intelligence at the edge"
    - "nonconvex and nonsmooth sparse optimization via adaptively iterative reweighted methods"
    - "broadband analog aggregation for low latency federated edge learning extended version"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "federated learning with non iid data"

Connected through hyperedge HE-2 (induced by source paper "toward dual functional radar communication systems optimal waveform design"):
  with members:
    - "adaptive interference removal for uncoordinated radar communication coexistence"
    - "joint design of overlaid communication systems and pulsed radars"
    - "mu mimo communications with mimo radar from co existence to joint transmission"
    - "robust mimo beamforming for cellular and radar coexistence"
    - "interference exploitation for radar and cellular coexistence the power efficient approach"
    - "an efficient manifold algorithm for constructive interference based constant envelope precoding"
    - "optimum co design for spectrum sharing between matrix completion based mimo radars and a mimo communication system"
    - "riemannian dictionary learning and sparse coding for positive definite matrices"

Connected through hyperedge HE-3 (induced by source paper "layered group sparse beamforming for cache enabled green wireless networks"):
  with members:
    - "fog aided wireless networks for content delivery fundamental latency tradeoffs"
    - "flexible cache aided networks with backhauling"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "cache aided interference channels"
    - "5g wireless backhaul networks challenges and research advances"
    - "two layer feature reduction for sparse group lasso via decomposition of convex sets"
    - "backhaul limited asymmetric cooperation for mimo cellular networks via semidefinite relaxation"
    - "sparse beamforming and user centric clustering for downlink cloud radio access network"

Connected through hyperedge HE-4 (induced by source paper "a d2d based protocol for ultra reliable wireless communications for industrial automation"):
  with members:
    - "massive connectivity with massive mimo part ii achievable rate characterization"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "a random access protocol for pilot allocation in crowded massive mimo systems"
    - "real time cooperative communication for automation over wireless"
    - "cloud radio access network virtualizing wireless access for dense heterogeneous systems"
    - "massive mimo for next generation wireless systems"
    - "ultra reliable communication in 5g wireless systems"
    - "joint power and admission control via linear programming deflation"

Connected through hyperedge HE-5 (induced by source paper "joint base station clustering and beamforming for non orthogonal multicast and unicast transmission with backhaul constraints"):
  with members:
    - "energy efficient joint unicast and multicast beamforming with multi antenna user terminals"
    - "joint beamforming and broadcasting in massive mimo"
    - "robust transmission in downlink multiuser miso systems a rate splitting approach"
    - "rate splitting for mimo wireless networks a promising phy layer strategy for lte evolution"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "sum rate maximization for mu miso with partial csit using joint multicasting and broadcasting"
    - "sparse beamforming and user centric clustering for downlink cloud radio access network"

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)