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 "on optimal scheduling for joint spatial division and multiplexing approach in fdd massive mimo"):
  Abstract: Massive multiple-input and multiple-output is widely considered as a key enabler of the next generation 5G networks. With a large number of antennas at the base station, both spectral and energy efficiencies can be enhanced. Unfortunately, the downlink channel estimation overhead scales linearly with the number of antennas. This burden is easily mitigated in time division duplex systems by the use of the channel reciprocity property. However, this is unfeasible for frequency division duplex systems, and the method of two-stage beamforming was therefore developed to reduce the amount of channel state information feedback. The performance of this scheme being highly dependent on the users grouping and scheduling mechanisms, we introduce in this paper a new similarity measure coupled with a novel clustering procedure to achieve the appropriate users grouping. We also proceed to formulate the optimal users scheduling policy in joint spatial division and multiplexing (JSDM) and prove that it is NP-hard. This result is of paramount importance since it suggests that, unless P = NP, there are no polynomial time algorithms that solve the general scheduling problem to global optimality and the use of sub-optimal scheduling strategies is more realistic in practice. We, therefore, use graph theory to develop a sub-optimal users scheduling scheme that runs in polynomial time and outperforms the scheduling schemes previously introduced in the literature for JSDM in both sum-rate and throughput fairness.

Includes members:
  Member "graph theory based approach to users grouping and downlink scheduling in fdd massive mimo":
    also connected through other hyperedges:
      (none)
  Member "fdd massive mimo efficient downlink probing and uplink feedback via active channel sparsification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification")
  Member "efficient downlink channel probing and uplink feedback in fdd massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi band covariance interpolation with applications in massive mimo")
  Member "antenna grouping based feedback compression for fdd based massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a covariance based hybrid channel feedback in fdd massive mimo systems")
  Member "content aware user clustering and caching in wireless small cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cooperative caching in fog radio access networks a graph based approach")
  Member "scaling up mimo opportunities and challenges with very large arrays":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "downlink secrecy rate of one bit massive mimo system with active eavesdropping")
  Member "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "covariance aided csi acquisition with non orthogonal pilots in massive mimo a large system performance analysis")
  Member "energy and spectral efficiency of very large multiuser mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "downlink secrecy rate of one bit massive mimo system with active eavesdropping")

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)