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: "dynamic nested clustering for parallel phy layer processing in cloud rans"
  Abstract: Featured by centralized processing and cloud based infrastructure, Cloud Radio Access Network (C-RAN) is a promising solution to achieve an unprecedented system capacity in future wireless cellular networks. The huge capacity gain mainly comes from the centralized and coordinated signal processing at the cloud server. However, full-scale coordination in a large-scale C-RAN requires the processing of very large channel matrices, leading to high computational complexity and channel estimation overhead. To resolve this challenge, we exploit the near-sparsity of large C-RAN channel matrices, and derive a unified theoretical framework for clustering and parallel processing. Based on the framework, we propose a dynamic nested clustering (DNC) algorithm that not only greatly improves the system scalability in terms of baseband-processing and channel-estimation complexity, but also is amenable to various parallel processing strategies for different data center architectures. With the proposed algorithm, we show that the computation time for the optimal linear detector is greatly reduced from $O(N^3)$ to no higher than $O(N^{\frac{42}{23}})$, where $N$ is the number of RRHs in C-RAN.

Connected through hyperedge HE-1 (induced by source paper "joint millimeter wave fronthaul and ofdma resource allocation in ultra dense cran"):
  with members:
    - "locally orthogonal training design for cloud rans based on graph coloring"
    - "millimeter wave wireless communications new results for rural connectivity"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "wireless communications in the era of big data"
    - "5g ultra dense cellular networks"
    - "large scale convex optimization for ultra dense cloud ran"
    - "optimized uplink transmission in multi antenna c ran with spatial compression and forward"
    - "optimized backhaul compression for uplink cloud radio access network"

Connected through hyperedge HE-2 (induced by source paper "fronthaul limited uplink ofdma in ultra dense cran with hybrid decoding"):
  with members:
    - "cross layer design for downlink multihop cloud radio access networks with network coding"
    - "joint millimeter wave fronthaul and ofdma resource allocation in ultra dense cran"
    - "recent advances in cloud radio access networks system architectures key techniques and open issues"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "green ofdma resource allocation in cache enabled cran"
    - "wireless communications in the era of big data"
    - "large scale convex optimization for ultra dense cloud ran"
    - "optimized uplink transmission in multi antenna c ran with spatial compression and forward"

Connected through hyperedge HE-3 (induced by source paper "channel matrix sparsity with imperfect channel state information in cloud radio access networks"):
  with members:
    - "energy efficient resource allocation optimization for multimedia heterogeneous cloud radio access networks"
    - "cluster content caching an energy efficient approach to improve quality of service in cloud radio access networks"
    - "inter tier interference suppression in heterogeneous cloud radio access networks"
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "large scale convex optimization for dense wireless cooperative networks"
    - "robust group sparse beamforming for multicast green cloud ran with imperfect csi"
    - "system architecture and key technologies for 5g heterogeneous cloud radio access networks"
    - "training design and channel estimation in uplink cloud radio access networks"

Connected through hyperedge HE-4 (induced by source paper "message passing in c ran joint user activity and signal detection"):
  with members:
    - "random access in c ran for user activity detection with limited capacity fronthaul"
    - "a lse and sparse message passing based channel estimation for mmwave mimo systems"
    - "gaussian message passing iterative detection for mimo noma systems with massive access"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "performance limits of compressive sensing channel estimation in dense cloud ran"):
  with members:
    - "recent advances in cloud radio access networks system architectures key techniques and open issues"
    - "robust iterative interference alignment for cellular networks with limited feedback"
    - "sparse signal processing concepts for efficient 5g system design"
    - "exact performance analysis of the oracle receiver for compressed sensing reconstruction"

Connected through hyperedge HE-6 (induced by source paper "centralized and distributed sparsification for low complexity message passing algorithm in c ran architectures"):
  with members:
    - "scalable uplink signal detection in c rans via randomized gaussian message passing"

Connected through hyperedge HE-7 (induced by source paper "an overview of transmission theory and techniques of large scale antenna systems for 5g wireless communications"):
  with members:
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
    - "mixed adc massive mimo detectors performance analysis and design optimization"
    - "performance analysis and location optimization for massive mimo systems with circularly distributed antennas"
    - "uplink achievable rate for massive mimo systems with low resolution adc"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "a stochastic geometry framework for analyzing pairwise cooperative cellular networks"
    - "structured compressive sensing based superimposed pilot design in downlink large scale mimo systems"
    - "fractional pilot reuse in massive mimo systems"

Connected through hyperedge HE-8 (induced by source paper "locally orthogonal training design for cloud rans based on graph coloring"):
  with members:
    - "rrh based massive mimo with on the fly pilot contamination control"
    - "cell free massive mimo versus small cells"
    - "selective uplink training for massive mimo systems"
    - "superimposed pilots are superior for mitigating pilot contamination in massive mimo part i theory and channel estimation"
    - "fundamental limits of training based multiuser mimo systems"
    - "training design and channel estimation in uplink cloud radio access networks"
    - "joint power and antenna selection optimization in large cloud radio access networks"
    - "blind pilot decontamination"

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)