human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "backpressure flow control"):
  Abstract: Effective congestion control in a multi-tenant data center is becoming increasingly challenging with rapidly increasing workload demand, ever faster links, small average transfer sizes, extremely bursty traffic, limited switch buffer capacity, and one-way protocols such as RDMA. Existing deployed algorithms, such as DCQCN, are still far from optimal in many plausible scenarios, particularly for tail latency. Many operators compensate by running their networks at low average utilization, dramatically increasing costs. In this paper, we argue that we have reached the practical limits of end to end congestion control. Instead, we propose a new clean slate design based on hop-by-hop per-flow flow control. We show that our approach achieves near optimal tail latency behavior even under challenging conditions such as high average link utilization and incast cross traffic. By contrast with prior hop-by-hop schemes, our main innovation is to show that per-flow flow control can be achieved with limited metadata and packet buffering. Further, we show that our approach generalizes well to cross-data center communication.

Includes members:
  Member "homa a receiver driven low latency transport protocol using network priorities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on recent advances in transport layer protocols")
  Member "revisiting network support for rdma":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scaling distributed machine learning with in network aggregation")

Question: Please tell me which class the source paper 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)