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: "a novel alternative to cloud ran for throughput densification coded pilots and fast user packet scheduling at remote radio heads"
  Abstract: We consider wireless networks of remote radio heads (RRH) with large antenna-arrays, operated in TDD, with uplink (UL) training and channel-reciprocity based downlink (DL) transmission. To achieve large area spectral efficiencies, we advocate the use of methods that rely on rudimentary scheduling, decentralized operation at each RRH and user-centric DL transmission. #R##N#A slotted system is assumed, whereby users are randomly scheduled (e.g., via shuffled round robin) in slots and across the limited pilot dimensions per slot. As a result, multiple users in the vicinity of an RRH can simultaneously transmit pilots on the same pilot dimension (and thus interfering with one another). Each RRH performs rudimentary processing of the pilot observations in "sectors". In a sector, the RRH is able to resolve a scheduled user's channel when that user is determined to be the only one among the scheduled users (on the same pilot dimension) with significant received power in the sector. Subsequently, only the subset of scheduled users whose channels are resolved in at least one sector can be served by the system. #R##N#We consider a spatially consistent evaluation of the area multiplexing gains by means of a Poisson Point Process (PPP) problem formulation where RRHs, blockers, scatterers and scheduled user terminals are all PPPs with individual intensities. Also, we study directional training at the user terminals. Our simulations suggest that, by controlling the intensity of the scheduled user PPP and the user-pilot beam-width, many fold improvements can be expected in area multiplexing gains with respect to conventional spatial pilot reuse systems.

Connected through hyperedge HE-1 (induced by source paper "fog massive mimo a user centric seamless hot spot architecture"):
  with members:
    - "realizing the tactile internet haptic communications over next generation 5g cellular networks"
    - "modeling and analysis of cellular networks using stochastic geometry a tutorial"
    - "ergodic spectral efficiency in mimo cellular networks"
    - "unique coverage in boolean models"
    - "system level performance evaluation of lte v2x network"
    - "an uplink downlink duality for cloud radio access network"
    - "rrh based massive mimo with on the fly pilot contamination control"
    - "directional training and fast sector based processing schemes for mmwave channels"

Connected through hyperedge HE-2 (induced by source paper "a graph theoretic approach for training overhead reduction in fdd massive mimo systems"):
  with members:
    - "directional training and fast sector based processing schemes for mmwave channels"
    - "massive mimo ten myths and one critical question"
    - "joint csit acquisition based on low rank matrix completion for fdd massive mimo systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "joint spatial division and multiplexing for mm wave channels"

Connected through hyperedge HE-3 (induced by source paper "efficient c ran random access for iot devices learning links via recommendation systems"):
  with members:
    - "compressive channel estimation and multi user detection in c ran"
    - "user activity detection in massive random access compressed sensing vs coded slotted aloha"
    - "rrh based massive mimo with on the fly pilot contamination control"
    - "random access protocols for massive mimo"
    - "directional training and fast sector based processing schemes for mmwave channels"
    - "user association and interference management in massive mimo hetnets"
    - "optimal user cell association for massive mimo wireless networks"
    - "analysis of blockage effects on urban cellular networks"

Connected through hyperedge HE-4 (induced by source paper "directional training and fast sector based processing schemes for mmwave channels"):
  with members:
    - "on the performance of cell free massive mimo with short term power constraints"
    - "rrh based massive mimo with on the fly pilot contamination control"
    - "compressed sensing based pilot assignment and reuse for mobile ues in mmwave cellular systems"
    - "3 d statistical channel model for millimeter wave outdoor mobile broadband communications"
    - "scalable synchronization and reciprocity calibration for distributed multiuser mimo"
    - "joint spatial division and multiplexing opportunistic beamforming and user grouping"
    - "joint spatial division and multiplexing for mm wave channels"
    - "multiuser mimo achievable rates with downlink training and channel state feedback"

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)