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: "revisiting network support for rdma"
  Abstract: The advent of RoCE (RDMA over Converged Ethernet) has led to a significant increase in the use of RDMA in datacenter networks. To achieve good performance, RoCE requires a lossless network which is in turn achieved by enabling Priority Flow Control (PFC) within the network. However, PFC brings with it a host of problems such as head-of-the-line blocking, congestion spreading, and occasional deadlocks. Rather than seek to fix these issues, we instead ask: is PFC fundamentally required to support RDMA over Ethernet? #R##N#We show that the need for PFC is an artifact of current RoCE NIC designs rather than a fundamental requirement. We propose an improved RoCE NIC (IRN) design that makes a few simple changes to the RoCE NIC for better handling of packet losses. We show that IRN (without PFC) outperforms RoCE (with PFC) by 6-83% for typical network scenarios. Thus not only does IRN eliminate the need for PFC, it improves performance in the process! We further show that the changes that IRN introduces can be implemented with modest overheads of about 3-10% to NIC resources. Based on our results, we argue that research and industry should rethink the current trajectory of network support for RDMA.

Connected through hyperedge HE-1 (induced by source paper "scaling distributed machine learning with in network aggregation"):
  with members:
    - "beyond data and model parallelism for deep neural networks"
    - "signsgd compressed optimisation for non convex problems"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "in datacenter performance analysis of a tensor processing unit"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "mixed precision training"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-2 (induced by source paper "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"):
  with members:
    - "slim fly a cost effective low diameter network topology"
    - "expander datacenters from theory to practice"
    - "homa a receiver driven low latency transport protocol using network priorities"
    - "a survey and evaluation of data center network topologies"
    - "measuring and understanding throughput of network topologies"
    - "enabling highly scalable remote memory access programming with mpi 3 one sided"
    - "jellyfish networking data centers randomly"

Connected through hyperedge HE-3 (induced by source paper "rdma performance isolation with justitia"):
  with members:
    - "datacenter rpcs can be general and fast"
    - "tensorflow a system for large scale machine learning"
    - "finishing flows quickly with preemptive scheduling"

Connected through hyperedge HE-4 (induced by source paper "backpressure flow control"):
  with members:
    - "homa a receiver driven low latency transport protocol using network priorities"

Connected through hyperedge HE-5 (induced by source paper "dart divide and specialize for fast response to congestion in rdma based datacenter networks"):
  with members:
    - "universal packet scheduling"
    - "finishing flows quickly with preemptive scheduling"

Connected through hyperedge HE-6 (induced by source paper "datacenter rpcs can be general and fast"):
  with members:
    - "the end of a myth distributed transactions can scale"
    - "the end of slow networks it s time for a redesign"

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.NI(Networking and Internet Architecture)