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 i theory"):
  Abstract: In this two-part paper, we propose a general algorithmic framework for the minimization of a nonconvex smooth function subject to nonconvex smooth constraints. The algorithm solves a sequence of (separable) strongly convex problems and mantains feasibility at each iteration. Convergence to a stationary solution of the original nonconvex optimization is established. Our framework is very general and flexible; it unifies several existing Successive Convex Approximation (SCA)-based algorithms such as (proximal) gradient or Newton type methods, block coordinate (parallel) descent schemes, difference of convex functions methods, and improves on their convergence properties. More importantly, and differently from current SCA approaches, it naturally leads to distributed and parallelizable implementations for a large class of nonconvex problems. #R##N#This Part I is devoted to the description of the framework in its generality. In Part II we customize our general methods to several multi-agent optimization problems, mainly in communications and networking; the result is a new class of (distributed) algorithms that compare favorably to existing ad-hoc (centralized) schemes (when they exist).

Includes members:
  Member "signal processing and optimal resource allocation for the interference channel":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "one bit over the air aggregation for communication efficient federated edge learning design and convergence analysis")
  Member "decomposition by partial linearization parallel optimization of multi agent systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convergent policy optimization for safe reinforcement learning")
  Member "distributed optimal beamformers for cognitive radios robust to channel uncertainties":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "context aware decentralized invariant signaling for opportunistic communications")
  Member "decomposition by successive convex approximation a unifying approach for linear transceiver design in heterogeneous networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a stochastic analysis of network mimo systems")
  Member "linear transceiver design for a mimo interfering broadcast channel achieving max min fairness":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "load balancing user association in millimeter wave mimo networks")
  Member "optimal distributed beamforming for miso interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multiuser wirelessly powered backscatter communications nonlinearity waveform design and sinr energy tradeoff")

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.MA(Multiagent Systems)