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: "a distributed asynchronous and incremental algorithm for nonconvex optimization an admm based approach"
  Abstract: The alternating direction method of multipliers (ADMM) has been popular for solving many signal processing problems, convex or nonconvex. In this paper, we study an asynchronous implementation of the ADMM for solving a nonconvex nonsmooth optimization problem, whose objective is the sum of a number of component functions. The proposed algorithm allows the problem to be solved in a distributed, asynchronous and incremental manner. First, the component functions can be distributed to different computing nodes, who perform the updates asynchronously without coordinating with each other. Two sources of asynchrony are covered by our algorithm: one is caused by the heterogeneity of the computational nodes, and the other arises from unreliable communication links. Second, the algorithm can be viewed as implementing an incremental algorithm where at each step the (possibly delayed) gradients of only a subset of component functions are update d. We show that when certain bounds are put on the level of asynchrony, the proposed algorithm converges to the set of stationary solutions (resp. optimal solutions) for the nonconvex (resp. convex) problem. To the best of our knowledge, the proposed ADMM implementation can tolerate the highest degree of asynchrony, among all known asynchronous variants of the ADMM. Moreover, it is the first ADMM implementation that can deal with nonconvexity and asynchrony at the same time.

Connected through hyperedge HE-1 (induced by source paper "distributed asynchronous dual free stochastic dual coordinate ascent"):
  with members:
    - "sdca without duality regularization and individual convexity"
    - "asynchronous stochastic gradient descent with variance reduction for non convex optimization"
    - "adding vs averaging in distributed primal dual optimization"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "asynchronous distributed semi stochastic gradient optimization"
    - "sdca without duality"
    - "distributed mini batch sdca"
    - "improved svrg for non strongly convex or sum of non convex objectives"

Connected through hyperedge HE-2 (induced by source paper "asynchronous stochastic gradient descent with variance reduction for non convex optimization"):
  with members:
    - "distributed asynchronous dual free stochastic dual coordinate ascent"
    - "decoupled asynchronous proximal stochastic gradient descent with variance reduction"
    - "asynchronous stochastic block coordinate descent with variance reduction"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "asynchronous distributed semi stochastic gradient optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-3 (induced by source paper "asynchronous distributed admm for large scale optimization part i algorithm and convergence analysis"):
  with members:
    - "asynchronous distributed admm for large scale optimization part ii linear convergence analysis and numerical performance"
    - "distributed constrained optimization by consensus based primal dual perturbation method"
    - "decomposition by partial linearization parallel optimization of multi agent systems"
    - "asynchronous distributed optimization using a randomized alternating direction method of multipliers"

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.IT(Information Theory)