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: "communication computation tradeoffs in consensus based distributed optimization"
  Abstract: We study the scalability of consensus-based distributed optimization algorithms by considering two questions: How many processors should we use for a given problem, and how often should they communicate when communication is not free? Central to our analysis is a problem-specific value $r$ which quantifies the communication/computation tradeoff. We show that organizing the communication among nodes as a $k$-regular expander graph (Reingold, Vadhan, and Wigderson, 2002) yields speedups, while when all pairs of nodes communicate (as in a complete graph), there is an optimal number of processors that depends on $r$. Surprisingly, a speedup can be obtained, in terms of the time to reach a fixed level of accuracy, by communicating less and less frequently as the computation progresses. Experiments on a real cluster solving metric learning and non-smooth convex minimization tasks demonstrate strong agreement between theory and practice.

Connected through hyperedge HE-1 (induced by source paper "a survey on distributed machine learning"):
  with members:
    - "mxnet mpi embedding mpi parallelism in parameter server task model for scaling deep learning"
    - "how to backdoor federated learning"
    - "no peek a survey of private distributed deep learning"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "scaling deep learning on gpu and knights landing clusters"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-2 (induced by source paper "trajectory normalized gradients for distributed optimization"):
  with members:
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "an alternative view when does sgd escape local minima"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"
    - "error compensated quantized sgd and its applications to large scale distributed optimization"
    - "self normalizing neural networks"
    - "gradient sparsification for communication efficient distributed optimization"
    - "sparse communication for distributed gradient descent"

Connected through hyperedge HE-3 (induced by source paper "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"):
  with members:
    - "don t use large mini batches use local sgd"
    - "on the convergence properties of a k step averaging stochastic gradient descent algorithm for nonconvex optimization"
    - "signsgd compressed optimisation for non convex problems"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "gradient sparsification for communication efficient distributed optimization"
    - "parle parallelizing stochastic gradient descent"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "cocoa a general framework for communication efficient distributed optimization"

Connected through hyperedge HE-4 (induced by source paper "distributed machine learning via sufficient factor broadcasting"):
  with members:
    - "lshtc a benchmark for large scale text classification"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"
    - "high performance distributed ml at scale through parameter server consistency models"
    - "communication efficient distributed optimization using an approximate newton type method"

Connected through hyperedge HE-5 (induced by source paper "efficient distributed online prediction and stochastic optimization with approximate distributed averaging"):
  with members:
    - "adding vs averaging in distributed primal dual optimization"
    - "communication efficient distributed dual coordinate ascent"
    - "fast distributed gradient methods"
    - "distributed autonomous online learning regrets and intrinsic privacy preserving properties"
    - "optimal distributed online prediction using mini batches"
    - "gossip algorithms for distributed signal processing"
    - "optimization and analysis of distributed averaging with short node memory"

Connected through hyperedge HE-6 (induced by source paper "deep learning of representations looking forward"):
  with members:
    - "a semantic matching energy function for learning with multi relational data"
    - "knowledge matters importance of prior information for optimization"
    - "big neural networks waste capacity"
    - "deep generative stochastic networks trainable by backprop"
    - "generalized denoising auto encoders as generative models"
    - "estimating or propagating gradients through stochastic neurons"
    - "stochastic pooling for regularization of deep convolutional neural networks"
    - "revisiting natural gradient for deep networks"

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)