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 frank wolfe algorithm for communication efficient sparse learning"
  Abstract: Learning sparse combinations is a frequent theme in machine learning. In this paper, we study its associated optimization problem in the distributed setting where the elements to be combined are not centrally located but spread over a network. We address the key challenges of balancing communication costs and optimization errors. To this end, we propose a distributed Frank-Wolfe (dFW) algorithm. We obtain theoretical guarantees on the optimization error $\epsilon$ and communication cost that do not depend on the total number of combining elements. We further show that the communication cost of dFW is optimal by deriving a lower-bound on the communication cost required to construct an $\epsilon$-approximate solution. We validate our theoretical analysis with empirical studies on synthetic and real-world data, which demonstrate that dFW outperforms both baselines and competing methods. We also study the performance of dFW when the conditions of our analysis are relaxed, and show that dFW is fairly robust.

Connected through hyperedge HE-1 (induced by source paper "communication efficient asynchronous stochastic frank wolfe over nuclear norm balls"):
  with members:
    - "quantized frank wolfe communication efficient distributed optimization"
    - "quantized frank wolfe faster optimization lower communication and projection free"
    - "a distributed frank wolfe framework for learning low rank matrices with the trace norm"
    - "algorithmic regularization in over parameterized matrix recovery"
    - "algorithmic regularization in over parameterized matrix sensing and neural networks with quadratic activations"
    - "linear convergence of a frank wolfe type algorithm over trace norm balls"
    - "variance reduced and projection free stochastic optimization"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"

Connected through hyperedge HE-2 (induced by source paper "quantized frank wolfe faster optimization lower communication and projection free"):
  with members:
    - "constrained deep learning using conditional gradient and applications in computer vision"
    - "deep frank wolfe for neural network optimization"
    - "a distributed frank wolfe framework for learning low rank matrices with the trace norm"
    - "neural conditional gradients"
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "learning infinite rbms with frank wolfe"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"

Connected through hyperedge HE-3 (induced by source paper "a distributed frank wolfe framework for learning low rank matrices with the trace norm"):
  with members:
    - "variance reduced and projection free stochastic optimization"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"
    - "projection free online learning"
    - "recovering low rank matrices from few coefficients in any basis"
    - "phase retrieval via matrix completion"
    - "the power of convex relaxation near optimal matrix completion"
    - "a simpler approach to matrix completion"

Connected through hyperedge HE-4 (induced by source paper "distributed multi task learning with shared representation"):
  with members:
    - "communication efficient distributed dual coordinate ascent"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "multi target regression via input space expansion treating targets as inputs"
    - "distributed learning communication complexity and privacy"
    - "natural language processing almost from scratch"
    - "feature hashing for large scale multitask learning"

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.DC(Distributed, Parallel, and Cluster Computing)