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: "differentiated latency in data center networks with erasure coded files through traffic engineering"
  Abstract: This paper proposes an algorithm to minimize weighted service latency for different classes of tenants (or service classes) in a data center network where erasure-coded files are stored on distributed disks/racks and access requests are scattered across the network. Due to the limited bandwidth available at both top-of-the-rack and aggregation switches, and differentiated service requirements of the tenants, network bandwidth must be apportioned among different intra- and inter-rack data flows for different service classes in line with their traffic statistics. We formulate this problem as weighted queuing and employ a class of probabilistic request scheduling policies to derive a closed-form upper-bound of service latency for erasure-coded storage with arbitrary file access patterns and service time distributions. The result enables us to propose a joint weighted latency (over different service classes) optimization over three entangled “control knobs”: the bandwidth allocation at top-of-the-rack and aggregation switches for different service classes, dynamic scheduling of file requests, and the placement of encoded file chunks (i.e., data locality). The joint optimization is shown to be a mixed-integer problem. We develop an iterative algorithm which decouples and solves the joint optimization as 3 sub-problems, which are either convex or solvable via bipartite matching in polynomial time. The proposed algorithm is prototyped in an open-source, distributed file system, Tahoe, and evaluated on a cloud testbed with 16 separate physical hosts in an OpenStack cluster using Cisco switches. Experiments validate our theoretical latency analysis and show significant latency reduction for diverse file access patterns. The results provide valuable insights on designing low-latency data center networks with erasure coded storage.

Connected through hyperedge HE-1 (induced by source paper "resource allocation in multigranular optical networks"):
  with members:
    - "a reconfigurable high performance optical data center architecture"
    - "chronos a unifying optimization framework for speculative execution of deadline critical mapreduce jobs"
    - "on the optimality of scheduling dependent mapreduce tasks on heterogeneous machines"
    - "joint latency and cost optimization for erasure coded data center storage"
    - "sprout a functional caching approach to minimize service latency in erasure coded storage"

Connected through hyperedge HE-2 (induced by source paper "tiered cloud storage via two stage latency aware bidding"):
  with members:
    - "video streaming in distributed erasure coded storage systems stall duration analysis"
    - "joint latency and cost optimization for erasure coded data center storage"
    - "sprout a functional caching approach to minimize service latency in erasure coded storage"
    - "tail index for a distributed storage system with pareto file size distribution"
    - "time and location aware mobile data pricing"

Connected through hyperedge HE-3 (induced by source paper "sprout a functional caching approach to minimize service latency in erasure coded storage"):
  with members:
    - "taming tail latency for erasure coded distributed storage systems"
    - "joint latency and cost optimization for erasure coded data center storage"
    - "coded caching with distributed storage"
    - "on storage allocation for maximum service rate in distributed storage systems"
    - "tail index for a distributed storage system with pareto file size distribution"
    - "multi server coded caching"
    - "coded caching for delay sensitive content"
    - "hierarchical coded caching"

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.DC(Distributed, Parallel, and Cluster Computing)