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: "scaling distributed machine learning with in network aggregation"
  Abstract: Training complex machine learning models in parallel is an increasingly important workload. We accelerate distributed parallel training by designing a communication primitive that uses a programmable switch dataplane to execute a key step of the training process. Our approach, SwitchML, reduces the volume of exchanged data by aggregating the model updates from multiple workers in the network. We co-design the switch processing with the end-host protocols and ML frameworks to provide a robust, efficient solution that speeds up training by up to 300%, and at least by 20% for a number of real-world benchmark models.

Connected through hyperedge HE-1 (induced by source paper "taurus an intelligent data plane"):
  with members:
    - "meta learning neural bloom filters"
    - "fpga based accelerators of deep learning networks for learning and classification a review"
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "experience driven networking a deep reinforcement learning based approach"
    - "in network neural networks"
    - "structured control nets for deep reinforcement learning"
    - "training deep neural networks with 8 bit floating point numbers"
    - "how many mice make an elephant modelling flow length and size distribution of internet traffic"

Connected through hyperedge HE-2 (induced by source paper "on biased compression for distributed learning"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "distributed learning with compressed gradient differences"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "advances and open problems in federated learning"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "natural compression for distributed deep learning"
    - "federated learning challenges methods and future directions"

Connected through hyperedge HE-3 (induced by source paper "natural compression for distributed deep learning"):
  with members:
    - "distributed learning with compressed gradient differences"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "tighter theory for local sgd on identical and heterogeneous data"
    - "99 of distributed optimization is a waste of time the issue and how to fix it"
    - "federated learning strategies for improving communication efficiency"
    - "not all samples are created equal deep learning with importance sampling"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "gradient sparsification for communication efficient distributed optimization"

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)