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 framework for learning low rank matrices with the trace norm"
  Abstract: We consider the problem of learning a high-dimensional but low-rank matrix from a large-scale dataset distributed over several machines, where low-rankness is enforced by a convex trace norm constraint. We propose DFW-Trace, a distributed Frank–Wolfe algorithm which leverages the low-rank structure of its updates to achieve efficiency in time, memory and communication usage. The step at the heart of DFW-Trace is solved approximately using a distributed version of the power method. We provide a theoretical analysis of the convergence of DFW-Trace, showing that we can ensure sublinear convergence in expectation to an optimal solution with few power iterations per epoch. We implement DFW-Trace in the Apache Spark distributed programming framework and validate the usefulness of our approach on synthetic and real data, including the ImageNet dataset with high-dimensional features extracted from a deep neural network.

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"
    - "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"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"

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"
    - "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"
    - "qsgd communication efficient sgd via gradient quantization and encoding"

Connected through hyperedge HE-3 (induced by source paper "neural conditional gradients"):
  with members:
    - "learning to optimize neural nets"
    - "optnet differentiable optimization as a layer in neural networks"
    - "linear convergence of a frank wolfe type algorithm over trace norm balls"
    - "learning to reinforcement learn"
    - "learning to learn by gradient descent by gradient descent"
    - "matching networks for one shot learning"
    - "adam a method for stochastic optimization"
    - "deep learning using linear support vector machines"

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)