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: "blink fast and generic collectives for distributed ml"
  Abstract: Model parameter synchronization across GPUs introduces high overheads for data-parallel training at scale. Existing parameter synchronization protocols cannot effectively leverage available network resources in the face of ever increasing hardware heterogeneity. To address this, we propose Blink, a collective communication library that dynamically generates optimal communication primitives by packing spanning trees. We propose techniques to minimize the number of trees generated and extend Blink to leverage heterogeneous communication channels for faster data transfers. Evaluations show that compared to the state-of-the-art (NCCL), Blink can achieve up to 8x faster model synchronization, and reduce end-to-end training time for image classification tasks by up to 40%.

Connected through hyperedge HE-1 (induced by source paper "hoplite efficient collective communication for task based distributed systems"):
  with members:
    - "towards scalable dataframe systems"
    - "pytorch an imperative style high performance deep learning library"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "tune a research platform for distributed model selection and training"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "ray a distributed framework for emerging ai applications"
    - "rllib abstractions for distributed reinforcement learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "pipelined training with stale weights of deep convolutional neural networks"):
  with members:
    - "training neural networks using features replay"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "pipedream fast and efficient pipeline parallel dnn training"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

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)