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 "locally orthogonal training design for cloud rans based on graph coloring"):
  Abstract: We consider training-based channel estimation for a cloud radio access network (CRAN), in which a large amount of remote radio heads (RRHs) and users are randomly scattered over the service area. In this model, assigning orthogonal training sequences to all users will incur a substantial overhead to the overall network, and is even impossible when the number of users is large. Therefore, in this paper, we introduce the notion of local orthogonality, under which the training sequence of a user is orthogonal to those of the other users in its neighborhood. We model the design of locally orthogonal training sequences as a graph coloring problem. Then, based on the theory of random geometric graph, we show that the minimum training length scales in the order of lnK, where K is the number of users covered by a CRAN. This indicates that the proposed training design yields a scalable solution to sustain the need of large-scale cooperation in CRANs. Numerical results show that the proposed scheme outperforms other reference schemes.

Includes members:
  Member "rrh based massive mimo with on the fly pilot contamination control":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "random access for massive machine type communications")
  Member "cell free massive mimo versus small cells":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analysis of uav communications in cell free massive mimo systems")
  Member "selective uplink training for massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "channel coherence classification with frame shifting in massive mimo systems")
  Member "superimposed pilots are superior for mitigating pilot contamination in massive mimo part i theory and channel estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on hybrid pilot for channel estimation in massive mimo uplink")
  Member "fundamental limits of training based multiuser mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "super resolution blind channel and signal estimation for massive mimo with one dimensional antenna array")
  Member "training design and channel estimation in uplink cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "channel matrix sparsity with imperfect channel state information in cloud radio access networks")
  Member "joint power and antenna selection optimization in large cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stochastic successive convex optimization for two timescale hybrid precoding in massive mimo")
  Member "dynamic nested clustering for parallel phy layer processing in cloud rans":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "joint millimeter wave fronthaul and ofdma resource allocation in ultra dense cran")

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)