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: "jellyfish networking data centers randomly"
  Abstract: Industry experience indicates that the ability to incrementally expand data centers is essential. However, existing high-bandwidth network designs have rigid structure that interferes with incremental expansion. We present Jellyfish, a high-capacity network interconnect, which, by adopting a random graph topology, yields itself naturally to incremental expansion. Somewhat surprisingly, Jellyfish is more cost-efficient than a fat-tree: A Jellyfish interconnect built using the same equipment as a fat-tree, supports as many as 25% more servers at full capacity at the scale of a few thousand nodes, and this advantage improves with scale. Jellyfish also allows great flexibility in building networks with different degrees of oversubscription. However, Jellyfish's unstructured design brings new challenges in routing, physical layout, and wiring. We describe and evaluate approaches that resolve these challenges effectively, indicating that Jellyfish could be deployed in today's data centers.

Connected through hyperedge HE-1 (induced by source paper "metteor robust multi traffic topology engineering for commercial data center networks"):
  with members:
    - "expanding across time to deliver bandwidth efficiency and low latency"
    - "space shuffle a scalable flexible and high performance data center network"
    - "measuring and understanding throughput of network topologies"

Connected through hyperedge HE-2 (induced by source paper "a survey of big data machine learning applications optimization in cloud data centers and networks"):
  with members:
    - "fog assisted caching employing solar renewable energy for delivering video on demand service"
    - "energy efficient and resilient infrastructure for fog computing health monitoring applications"
    - "virtual machines embedding for cloud pon awgr and server based data centres"
    - "a survey on geographically distributed big data processing using mapreduce"
    - "disaggregation for improved efficiency in fog computing era"
    - "experimental evaluation of server centric passive optical network based data centre architecture"
    - "energy efficient distributed processing in vehicular cloud architecture"
    - "energy efficient software matching in vehicular fog"

Connected through hyperedge HE-3 (induced by source paper "slim fly a cost effective low diameter network topology"):
  with members:
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "red blue pebbling revisited near optimal parallel matrix matrix multiplication"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "network accelerated non contiguous memory transfers"
    - "communication efficient jaccard similarity for high performance distributed genome comparisons"
    - "slim graph practical lossy graph compression for approximate graph processing storage and analytics"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"

Connected through hyperedge HE-4 (induced by source paper "renets toward statically optimal self adjusting networks"):
  with members:
    - "slim fly a cost effective low diameter network topology"
    - "datacenter traffic control understanding techniques and tradeoffs"
    - "toward demand aware networking a theory for self adjusting networks"
    - "demand aware network designs of bounded degree"
    - "online balanced repartitioning"
    - "high throughput data center topology design"
    - "competitive paging algorithms"

Connected through hyperedge HE-5 (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"
    - "revisiting network support for rdma"
    - "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"

Connected through hyperedge HE-6 (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"
    - "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"
    - "an axiomatic theory of fairness in network resource allocation"

Connected through hyperedge HE-7 (induced by source paper "scdp systematic rateless coding for efficient data transport in data centres"):
  with members:
    - "elmo source routed multicast for cloud services"
    - "liquid cloud storage"
    - "dual structure data center multicast using software defined networking"
    - "repflow minimizing flow completion times with replicated flows in data centers"

Connected through hyperedge HE-8 (induced by source paper "managing recurrent virtual network updates in multi tenant datacenters a system perspective"):
  with members:
    - "finishing flows quickly with preemptive scheduling"

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)