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 "distributed learning in non convex environments part ii polynomial escape from saddle points"):
  Abstract: The diffusion strategy for distributed learning from streaming data employs local stochastic gradient updates along with exchange of iterates over neighborhoods. In Part I [2] of this work we established that agents cluster around a network centroid and proceeded to study the dynamics of this point. We established expected descent in non-convex environments in the large-gradient regime and introduced a short-term model to examine the dynamics over finite-time horizons. Using this model, we establish in this work that the diffusion strategy is able to escape from strict saddle-points in O(1/$\mu$) iterations; it is also able to return approximately second-order stationary points in a polynomial number of iterations. Relative to prior works on the polynomial escape from saddle-points, most of which focus on centralized perturbed or stochastic gradient descent, our approach requires less restrictive conditions on the gradient noise process.

Includes members:
  Member "on nonconvex optimization for machine learning gradients stochasticity and saddle points":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond linearization on quadratic and higher order approximation of wide neural networks")
  Member "distributed stochastic optimization with gradient tracking over strongly connected networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gradient tracking and variance reduction for decentralized optimization and machine learning")
  Member "escaping saddles with stochastic gradients":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improve sgd training via aligning min batches")
  Member "how to escape saddle points efficiently":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")
  Member "proximal multitask learning over networks with sparsity inducing coregularization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "decentralized multi task learning based on extreme learning machines")
  Member "next in network nonconvex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gradient tracking and variance reduction for decentralized optimization and machine learning")
  Member "escaping from saddle points online stochastic gradient for tensor decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "on the learning behavior of adaptive networks part ii performance analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "inverse graph learning over optimization 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.MA(Multiagent Systems)