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 "generalized sparse and low rank optimization for ultra dense networks"):
  Abstract: Ultra-dense network (UDN) is a promising technology to further evolve wireless networks and meet the diverse performance requirements of 5G networks. With abundant access points, each with communication, computation and storage resources, UDN brings unprecedented benefits, including significant improvement in network spectral efficiency and energy efficiency, greatly reduced latency to enable novel mobile applications, and the capability of providing massive access for Internet of Things (IoT) devices. However, such great promises come with formidable research challenges. To design and operate such complex networks with various types of resources, efficient and innovative methodologies will be needed. This motivates the recent introduction of highly structured and generalizable models for network optimization. In this article, we present some recently proposed large-scale sparse and low-rank frameworks for optimizing UDNs, supported by various motivating applications. A special attention is paid on algorithmic approaches to deal with nonconvex objective functions and constraints, as well as computational scalability.

Includes members:
  Member "coding for distributed fog computing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "energy efficient user association and power allocation in millimeter wave based ultra dense networks with energy harvesting base stations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi antenna aided secrecy beamforming optimization for wirelessly powered hetnets")
  Member "low rank matrix completion for topological interference management by riemannian pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "low rank matrix completion a contemporary survey")
  Member "fronthauling for 5g lte u ultra dense cloud small cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint fronthaul multicast and cooperative beamforming for cache enabled cloud based small cell networks an mds codes aided approach")
  Member "an overview of low rank matrix recovery from incomplete observations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "large scale convex optimization for dense wireless cooperative networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "large scale beamforming for massive mimo via randomized sketching")
  Member "sparse signal processing concepts for efficient 5g system design":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")
  Member "topological interference management through index coding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the separability of parallel miso broadcast channels under partial csit a degrees of freedom region perspective")

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)