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: "ddccast meeting point to multipoint transfer deadlines across datacenters using alap scheduling policy"
  Abstract: Large cloud companies manage dozens of datacenters across the globe connected using dedicated inter-datacenter networks. An important application of these networks is data replication which is done for purposes such as increased resiliency via making backup copies, getting data closer to users for reduced delay and WAN bandwidth usage, and global load balancing. These replications usually lead to network transfers with deadlines that determine the time prior to which all datacenters should have a copy of the data. Inter-datacenter networks have limited capacity and need be utilized efficiently to maximize performance. In this report, we focus on applications that transfer multiple copies of objects from one datacenter to several datacenters given deadline constraints. Existing solutions are either deadline agnostic, or only consider point-to-point transfers. We propose DDCCast, a simple yet effective deadline aware point to multipoint technique based on DCCast and using ALAP traffic allocation. DDCCast performs careful admission control using temporal planning, uses rate-allocation and rate-limiting to avoid congestion and sends traffic over forwarding trees that are carefully selected to reduce bandwidth usage and maximize deadline meet rate. We perform experiments confirming DDCCast's potential to reduce total bandwidth usage by up to $45\%$ while admitting up to $25\%$ more traffic into the network compared to existing solutions that guarantee deadlines.

Connected through hyperedge HE-1 (induced by source paper "on efficient data transfers across geographically dispersed datacenters"):
  with members:
    - "quickcast fast and efficient inter datacenter transfers using forwarding tree cohorts"
    - "dccast efficient point to multipoint transfers across datacenters"
    - "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"
    - "quickcast fast and efficient inter datacenter transfers using forwarding tree cohorts"
    - "dccast efficient point to multipoint transfers across datacenters"
    - "multicast traffic engineering for software defined networks"

Connected through hyperedge HE-3 (induced by source paper "quickcast fast and efficient inter datacenter transfers using forwarding tree cohorts"):
  with members:
    - "datacenter traffic control understanding techniques and tradeoffs"
    - "gb pandas throughput and heavy traffic optimality analysis for affinity scheduling"
    - "near data scheduling for data centers with multiple levels of data locality"
    - "dccast efficient point to multipoint transfers across datacenters"
    - "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)