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 "parallel and distributed methods for nonconvex optimization part ii applications"):
  Abstract: In Part I of this paper, we proposed and analyzed a novel algorithmic framework for the minimization of a nonconvex (smooth) objective function, subject to nonconvex constraints, based on inner convex approximations. This Part II is devoted to the application of the framework to some resource allocation problems in communication networks. In particular, we consider two non-trivial case-study applications, namely: (generalizations of) i) the rate profile maximization in MIMO interference broadcast networks; and the ii) the max-min fair multicast multigroup beamforming problem in a multi-cell environment. We develop a new class of algorithms enjoying the following distinctive features: i) they are \emph{distributed} across the base stations (with limited signaling) and lead to subproblems whose solutions are computable in closed form; and ii) differently from current relaxation-based schemes (e.g., semidefinite relaxation), they are proved to always converge to d-stationary solutions of the aforementioned class of nonconvex problems. Numerical results show that the proposed (distributed) schemes achieve larger worst-case rates (resp. signal-to-noise interference ratios) than state-of-the-art centralized ones while having comparable computational complexity.

Includes members:
  Member "parallel selective algorithms for big data optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a randomized primal distributed algorithm for partitioned and big data non convex optimization")
  Member "parallel and distributed methods for nonconvex optimization part i theory":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimized video streaming over cloud a stall quality trade off")
  Member "decomposition by partial linearization parallel optimization of multi agent systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convergent policy optimization for safe reinforcement learning")
  Member "weighted fair multicast multigroup beamforming under per antenna power constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "signal processing for high throughput satellites challenges in new interference limited scenarios")
  Member "alternating rate profile optimization in single stream mimo interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multiuser wirelessly powered backscatter communications nonlinearity waveform design and sinr energy tradeoff")
  Member "coordinated multicast beamforming in multicell networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi antenna coded content delivery with caching a low complexity solution")
  Member "distributed optimal beamformers for cognitive radios robust to channel uncertainties":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "context aware decentralized invariant signaling for opportunistic communications")
  Member "sum rate maximization in two way af mimo relaying polynomial time solutions to a class of dc programming problems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "joint beamforming and power allocation in downlink noma multiuser mimo networks")

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)