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: "training design and channel estimation in uplink cloud radio access networks"
  Abstract: To decrease the training overhead and improve the channel estimation accuracy in uplink cloud radio access networks (C-RANs), a superimposed-segment training design is proposed. The core idea of the proposal is that each mobile station superimposes a periodic training sequence on the data signal, and each remote radio head prepends a separate pilot to the received signal before forwarding it to the centralized base band unit pool. Moreover, a complex-exponential basis-expansion-model based channel estimation algorithm to maximize a posteriori probability is developed. Simulation results show that the proposed channel estimation algorithm can effectively decrease the estimation mean square error and increase the average effective signal-to-noise ratio (AESNR) in C-RANs.

Connected through hyperedge HE-1 (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"
    - "large system analysis of base station cooperation for power minimization"

Connected through hyperedge HE-2 (induced by source paper "comsens exploiting pilot diversity for pervasive integration of communication and sensing in mimo tdd frameworks"):
  with members:
    - "optimizing pilot overhead for ultra reliable short packet transmission"
    - "pilot beam pattern design for channel estimation in massive mimo systems"
    - "channel estimation and optimal training design for correlated mimo two way relay systems in colored environment"

Connected through hyperedge HE-3 (induced by source paper "recent advances in cloud radio access networks system architectures key techniques and open issues"):
  with members:
    - "fronthaul compression and precoding design for c rans over ergodic fading channels"
    - "optimal fronthaul quantization for cloud radio positioning"
    - "inter tier interference suppression in heterogeneous cloud radio access networks"
    - "contract based interference coordination in heterogeneous cloud radio access networks"
    - "are heterogeneous cloud based radio access networks cost effective"
    - "energy efficient resource assignment and power allocation in heterogeneous cloud radio access networks"
    - "cooperative interference mitigation and handover management for heterogeneous cloud small cell networks"
    - "fronthaul constrained cloud radio access networks insights and challenges"

Connected through hyperedge HE-4 (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"
    - "joint power and antenna selection optimization in large cloud radio access networks"
    - "dynamic nested clustering for parallel phy layer processing in cloud rans"
    - "blind pilot decontamination"

Connected through hyperedge HE-5 (induced by source paper "contract based interference coordination in heterogeneous cloud radio access networks"):
  with members:
    - "system architecture and key technologies for 5g heterogeneous cloud radio access networks"
    - "joint power and antenna selection optimization in large cloud radio access networks"
    - "heterogeneous cloud radio access networks a new perspective for enhancing spectral and energy efficiencies"
    - "ergodic capacity analysis of remote radio head associations in cloud radio access networks"
    - "scalable synchronization and reciprocity calibration for distributed multiuser mimo"
    - "relay selection for ofdm wireless systems under asymmetric information a contract theory based approach"
    - "downlink coordinated multi point with overhead modeling in heterogeneous cellular networks"
    - "enhanced intercell interference coordination challenges in heterogeneous 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)