human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "realizing the tactile internet haptic communications over next generation 5g cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analyzing the tradeoffs in using millimeter wave directional links for high data rate tactile internet applications")
  Member "towards interconnected virtual reality opportunities challenges and enablers":
    also connected through other hyperedges:
      (none)
  Member "unique coverage in boolean models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fog massive mimo a user centric seamless hot spot architecture")
  Member "system level performance evaluation of lte v2x network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "enhancing video streaming in vehicular networks via resource slicing")
  Member "rrh based massive mimo with on the fly pilot contamination control":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "random access for massive machine type communications")
  Member "directional training and fast sector based processing schemes for mmwave channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fog massive mimo a user centric seamless hot spot architecture")
  Member "quantifying data rate and bandwidth requirements for immersive 5g experience":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fog massive mimo a user centric seamless hot spot architecture")
  Member "scalable synchronization and reciprocity calibration for distributed multiuser mimo":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "amplitude and phase estimation for absolute calibration of massive mimo front ends")

Question: Please tell me which class the source paper 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)