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: "high throughput data center topology design"
  Abstract: With high throughput networks acquiring a crucial role in supporting data-intensive applications, a variety of data center network topologies have been proposed to achieve high capacity at low cost. While this literature explores a large number of design points, even in the limited case of a network of identical switches, no proposal has been able to claim any notion of optimality. The case of heterogeneous networks, incorporating multiple line-speeds and port-counts as data centers grow over time, introduces even greater complexity. #R##N#In this paper, we present the first non-trivial upper-bound on network throughput under uniform traffic patterns for any topology with identical switches. We then show that random graphs achieve throughput surprisingly close to this bound, within a few percent at the scale of a few thousand servers. Apart from demonstrating that homogeneous topology design may be reaching its limits, this result also motivates our use of random graphs as building blocks to explore the design of heterogeneous networks. Given a heterogeneous pool of network switches, through experiments and analysis, we explore how the distribution of servers across switches and the interconnection of switches affect network throughput. We apply these insights to a real-world heterogeneous data center topology, VL2, demonstrating as much as 43% higher throughput with the same equipment.

Connected through hyperedge HE-1 (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"
    - "jellyfish networking data centers randomly"
    - "competitive paging algorithms"

Connected through hyperedge HE-2 (induced by source paper "expander datacenters from theory to practice"):
  with members:
    - "measuring and understanding throughput of network topologies"
    - "optimal networks from error correcting codes"

Connected through hyperedge HE-3 (induced by source paper "demand aware network designs of bounded degree"):
  with members:
    - "toward demand aware networking a theory for self adjusting networks"
    - "locally self adjusting skip graphs"
    - "obst a self adjusting peer to peer overlay based on multiple bsts"
    - "fast construction of nets in low dimensional metrics and their applications"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-4 (induced by source paper "space shuffle a scalable flexible and high performance data center network"):
  with members:
    - "achieving the optimal steaming capacity and delay using random regular digraphs in p2p networks"
    - "jellyfish networking data centers randomly"
    - "a quantitative measure of fairness and discrimination for resource allocation in shared computer systems"

Connected through hyperedge HE-5 (induced by source paper "measuring and understanding throughput of network topologies"):
  with members:
    - "slim fly a cost effective low diameter network topology"
    - "optimal networks from error correcting codes"
    - "jellyfish networking data centers randomly"
    - "systematic topology analysis and generation using degree correlations"

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)