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: "coordinated multicasting with opportunistic user selection in multicell wireless systems"
  Abstract: Physical layer multicasting with opportunistic user selection (OUS) is examined for multicell multi-antenna wireless systems. By adopting a two-layer encoding scheme, a rate-adaptive channel code is applied in each fading block to enable successful decoding by a chosen subset of users (which varies over different blocks) and an application layer erasure code is employed across multiple blocks to ensure that every user is able to recover the message after decoding successfully in a sufficient number of blocks. The transmit signal and code-rate in each block determine opportunistically the subset of users that are able to successfully decode and can be chosen to maximize the long-term multicast efficiency. The employment of OUS not only helps avoid rate-limitations caused by the user with the worst channel, but also helps coordinate interference among different cells and multicast groups. In this work, efficient algorithms are proposed for the design of the transmit covariance matrices, the physical layer code-rates, and the target user subsets in each block. In the single group scenario, the system parameters are determined by maximizing the group-rate, defined as the physical layer code-rate times the fraction of users that can successfully decode in each block. In the multi-group scenario, the system parameters are determined by considering a group-rate balancing optimization problem, which is solved by a successive convex approximation (SCA) approach. To further reduce the feedback overhead, we also consider the case where only part of the users feed back their channel vectors in each block and propose a design based on the balancing of the expected group-rates. In addition to SCA, a sample average approximation technique is also introduced to handle the probabilistic terms arising in this problem. The effectiveness of the proposed schemes is demonstrated by computer simulations.

Connected through hyperedge HE-1 (induced by source paper "joint multicast beamforming and user scheduling in large scale antenna systems"):
  with members:
    - "coordinated multicell multicast beamforming based on manifold optimization"
    - "multicast multigroup beamforming for per antenna power constrained large scale arrays"
    - "weighted fair multicast multigroup beamforming under per antenna power constraints"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "coordinated multicast beamforming in multicell networks"

Connected through hyperedge HE-2 (induced by source paper "rate splitting for max min fair multigroup multicast beamforming in overloaded systems"):
  with members:
    - "generalized multicast multibeam precoding for satellite communications"
    - "an overview on resource allocation techniques for multi user mimo systems"
    - "elevated multiplexing and signal space partitioning in the 2 user mimo ic with partial csit"
    - "sum rate maximization for linearly precoded downlink multiuser miso systems with partial csit a rate splitting approach"
    - "robust transmission in downlink multiuser miso systems a rate splitting approach"
    - "a rate splitting strategy for max min fair multigroup multicasting"
    - "overloaded multiuser miso transmission with imperfect csit"
    - "rate splitting for mimo wireless networks a promising phy layer strategy for lte evolution"

Connected through hyperedge HE-3 (induced by source paper "coordinated multicell multicast beamforming based on manifold optimization"):
  with members:
    - "weighted fair multicast multigroup beamforming under per antenna power constraints"
    - "coordinated multicast beamforming in multicell 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)