human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "distributed learning in the non convex world from batch to streaming data and beyond"):
  with members:
    - "decentralized stochastic gradient tracking for empirical risk minimization"
    - "gossipgrad scalable deep learning using gossip communication based asynchronous gradient descent"
    - "stochastic gradient push for distributed deep learning"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "byrdie byzantine resilient distributed coordinate descent for decentralized learning"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "federated optimization distributed optimization beyond the datacenter"
    - "multi agent distributed optimization via inexact consensus admm"

Connected through hyperedge HE-2 (induced by source paper "linear speedup in saddle point escape for decentralized non convex optimization"):
  with members:
    - "on nonconvex optimization for machine learning gradients stochasticity and saddle points"
    - "escaping saddles with stochastic gradients"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"
    - "next in network nonconvex optimization"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "communication efficient distributed dual coordinate ascent"
    - "on the learning behavior of adaptive networks part ii performance analysis"

Question: Please tell me which class the center node 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)