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: "quickcast fast and efficient inter datacenter transfers using forwarding tree cohorts"
  Abstract: Several organizations have built multiple datacenters connected via dedicated wide area networks over which large inter-datacenter transfers take place. Since many such transfers move the same data from one source to multiple destinations, using multicast forwarding trees can reduce bandwidth needs and improve completion times. However, using a single forwarding tree per transfer can lead to poor performance as the slowest receiver dictates the completion time for all receivers. Using multiple forwarding trees per transfer alleviates this concern-the average receiver could finish early; however, if done naively, bandwidth usage would also increase and it is apriori unclear how best to partition receivers, how to construct the multiple trees and how to determine the rate and schedule of flows on these trees. This paper presents QuickCast, a first solution to these problems. Using simulations on real-world network topologies, we see that QuickCast can speed up the average receiver's completion time by as much as  $10\times$  while only using  $1.04\times$  more bandwidth; further, the completion time for all receivers also improves by as much as  $1.57 >$  faster at high loads. Thereby, while some implementation challenges remain, we advocate using a cohort of forwarding trees.

Connected through hyperedge HE-1 (induced by source paper "on efficient data transfers across geographically dispersed datacenters"):
  with members:
    - "dccast efficient point to multipoint transfers across datacenters"
    - "ddccast meeting point to multipoint transfer deadlines across datacenters using alap scheduling policy"
    - "rcd rapid close to deadline scheduling for datacenter networks"
    - "multicast traffic engineering for software defined networks"
    - "finishing flows quickly with preemptive scheduling"
    - "jellyfish networking data centers randomly"
    - "an axiomatic theory of fairness in network resource allocation"

Connected through hyperedge HE-2 (induced by source paper "efficient inter datacenter bulk transfers with mixed completion time objectives"):
  with members:
    - "fast and efficient bulk multicasting over dedicated inter datacenter networks"
    - "dccast efficient point to multipoint transfers across datacenters"
    - "ddccast meeting point to multipoint transfer deadlines across datacenters using alap scheduling policy"
    - "multicast traffic engineering for software defined 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.NI(Networking and Internet Architecture)