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: "full duplex cloud radio access network stochastic design and analysis"
  Abstract: Full-duplex (FD) has emerged as a disruptive communications paradigm for enhancing the achievable spectral efficiency (SE), thanks to the recent major breakthroughs in self-interference (SI) mitigation. The FD versus half-duplex (HD) SE gain, in cellular networks, is however largely limited by the mutual-interference (MI) between the downlink (DL) and the uplink (UL). A potential remedy for tackling the MI bottleneck is through cooperative communications. This paper provides a stochastic design and analysis of FD enabled cloud radio access network (C-RAN) under the Poisson point process (PPP)-based abstraction model of multi-antenna radio units (RUs) and user equipments (UEs). We consider different disjoint and user-centric approaches towards the formation of finite clusters in the C-RAN. Contrary to most existing studies, we explicitly take into consideration non-isotropic fading channel conditions and finite-capacity fronthaul links. Accordingly, upper-bound expressions for the C-RAN DL and UL SEs, involving the statistics of all intended and interfering signals, are derived. The performance of the FD C-RAN is investigated through the proposed theoretical framework and Monte-Carlo (MC) simulations. The results indicate that significant FD versus HD C-RAN SE gains can be achieved, particularly in the presence of sufficient-capacity fronthaul links and advanced interference cancellation capabilities.

Connected through hyperedge HE-1 (induced by source paper "joint agc and receiver design for large scale mu mimo systems with coarsely quantized signals and c rans"):
  with members:
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "branch and bound precoding for multiuser mimo systems with 1 bit quantization"
    - "widely linear precoding for large scale mimo with iqi algorithms and performance analysis"
    - "uplink interference reduction in large scale antenna systems"
    - "iterative detection and decoding algorithms for mimo systems in block fading channels using ldpc codes"
    - "robust adaptive beamforming based on low rank and cross correlation techniques"
    - "cloud radio access network virtualizing wireless access for dense heterogeneous systems"
    - "adaptive reduced rank receive processing based on minimum symbol error rate criterion for large scale multiple antenna systems"

Connected through hyperedge HE-2 (induced by source paper "on the feasibility of full duplex large scale mimo cellular systems"):
  with members:
    - "multi cell massive mimo beamforming in assuring qos for large numbers of users"
    - "compact full duplex mimo radios in d2d underlaid cellular networks from system design to prototype results"
    - "massive mimo ten myths and one critical question"
    - "performance analysis of massive mimo for cell boundary users"
    - "prototyping real time full duplex radios"
    - "compressed channel feedback for correlated massive mimo systems"
    - "performance analysis of self interference cancellation methods in full duplex large scale mimo systems"
    - "opportunistic scheduling for full duplex uplink downlink 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)