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: "dccast efficient point to multipoint transfers across datacenters"
  Abstract: Using multiple datacenters allows for higher availability, load balancing and reduced latency to customers of cloud services. To distribute multiple copies of data, cloud providers depend on inter-datacenter WANs that ought to be used efficiently considering their limited capacity and the ever-increasing data demands. In this paper, we focus on applications that transfer objects from one datacenter to several datacenters over dedicated inter-datacenter networks. We present DCCast, a centralized Point to Multi-Point (P2MP) algorithm that uses forwarding trees to efficiently deliver an object from a source datacenter to required destination datacenters. With low computational overhead, DCCast selects forwarding trees that minimize bandwidth usage and balance load across all links. With simulation experiments on Google's GScale network, we show that DCCast can reduce total bandwidth usage and tail Transfer Completion Times (TCT) by up to $50\%$ compared to delivering the same objects via independent point-to-point (P2P) transfers.

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"
    - "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"
    - "quickcast fast and efficient inter datacenter transfers using forwarding tree cohorts"
    - "ddccast meeting point to multipoint transfer deadlines across datacenters using alap scheduling policy"
    - "multicast traffic engineering for software defined networks"

Connected through hyperedge HE-3 (induced by source paper "terra scalable cross layer gda optimizations"):
  with members:
    - "finishing flows quickly with preemptive scheduling"
    - "shark sql and rich analytics at scale"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-4 (induced by source paper "routing and scheduling of network flows with deadlines and discrete capacity allocation"):
  with members:
    - "finishing flows quickly with preemptive scheduling"

Connected through hyperedge HE-5 (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"
    - "ddccast meeting point to multipoint transfer deadlines across datacenters using alap scheduling policy"
    - "multicast traffic engineering for software defined networks"

Connected through hyperedge HE-6 (induced by source paper "gb pandas throughput and heavy traffic optimality analysis for affinity scheduling"):
  with members:
    - "near data scheduling for data centers with multiple levels of data locality"
    - "dcroute speeding up inter datacenter traffic allocation while guaranteeing deadlines"

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)