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: "coding for fast content download"
  Abstract: We study the fundamental trade-off between storage and content download time. We show that the download time can be significantly reduced by dividing the content into chunks, encoding it to add redundancy and then distributing it across multiple disks. We determine the download time for two content access models - the fountain and fork-join models that involve simultaneous content access, and individual access from enqueued user requests respectively. For the fountain model we explicitly characterize the download time, while in the fork-join model we derive the upper and lower bounds. Our results show that coding reduces download time, through the diversity of distributing the data across more disks, even for the total storage used.

Connected through hyperedge HE-1 (induced by source paper "a combinatorial view of the service rates of codes problem its equivalence to fractional matching and its connection with batch codes"):
  with members:
    - "a geometric view of the service rates of codes problem and its application to the service rate of the first order reed muller codes"
    - "load balancing performance in distributed storage with regular balanced redundancy"
    - "analyzing the download time of availability codes"
    - "service rate region of content access from erasure coded storage"
    - "simplex queues for hot data download"
    - "batch and pir codes and their connections to locally repairable codes"
    - "on distributed storage allocations of large files for maximum service rate"
    - "on the service capacity region of accessing erasure coded content"

Connected through hyperedge HE-2 (induced by source paper "stochastic gradient coding for straggler mitigation in distributed learning"):
  with members:
    - "fundamental limits of approximate gradient coding"
    - "analyzing the download time of availability codes"
    - "distributed stochastic gradient descent using ldgm codes"
    - "erasurehead distributed gradient descent without delays using approximate gradient coding"
    - "coded cooperative computation for internet of things"
    - "speeding up distributed machine learning using codes"
    - "anytime stochastic gradient descent a time to hear from all the workers"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"

Connected through hyperedge HE-3 (induced by source paper "private and rateless adaptive coded matrix vector multiplication"):
  with members:
    - "codedprivateml a fast and privacy preserving framework for distributed machine learning"
    - "analyzing the download time of availability codes"
    - "secure coded cooperative computation at the heterogeneous edge against byzantine attacks"
    - "an application of storage optimal matdot codes for coded matrix multiplication fast k nearest neighbors estimation"
    - "speeding up distributed machine learning using codes"
    - "anytime stochastic gradient descent a time to hear from all the workers"
    - "gasp codes for secure distributed matrix multiplication"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"

Connected through hyperedge HE-4 (induced by source paper "analyzing the download time of availability codes"):
  with members:
    - "straggler mitigation at scale"
    - "simplex queues for hot data download"
    - "on the service capacity region of accessing erasure coded content"
    - "rate optimal binary linear locally repairable codes with small availability"
    - "efficient redundancy techniques for latency reduction in cloud systems"
    - "provably delay efficient data retrieving in storage clouds"
    - "repair locality with multiple erasure tolerance"
    - "locality and availability in distributed storage"

Connected through hyperedge HE-5 (induced by source paper "optimizing redundancy levels in master worker compute clusters for straggler mitigation"):
  with members:
    - "scheduling with predictions and the price of misprediction"
    - "speeding up distributed machine learning using codes"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "bigroots an effective approach for root cause analysis of stragglers in big data system"
    - "straggler mitigation by delayed relaunch of tasks"
    - "near optimal straggler mitigation for distributed gradient methods"
    - "a machine learning approach to routing"
    - "coded convolution for parallel and distributed computing within a deadline"

Connected through hyperedge HE-6 (induced by source paper "straggler mitigation at scale"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "bigroots an effective approach for root cause analysis of stragglers in big data system"
    - "straggler mitigation by delayed relaunch of tasks"
    - "near optimal straggler mitigation for distributed gradient methods"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "gradient coding from cyclic mds codes and expander graphs"
    - "effective straggler mitigation which clones should attack and when"

Connected through hyperedge HE-7 (induced by source paper "delay optimal policies in partial fork join systems with redundancy and random slowdowns"):
  with members:
    - "mds coding is better than replication for job completion times"
    - "delay comparison of delivery and coding policies in data clusters"
    - "on delay optimal scheduling in queueing systems with replications"
    - "contrasting effects of replication in parallel systems from overload to underload and back"
    - "efficient redundancy techniques for latency reduction in cloud systems"
    - "using straggler replication to reduce latency in large scale parallel computing"
    - "efficient task replication for fast response times in parallel computation"
    - "when queueing meets coding optimal latency data retrieving scheme in storage clouds"

Connected through hyperedge HE-8 (induced by source paper "capacity of dynamical storage systems"):
  with members:
    - "liquid cloud storage"
    - "capacity of clustered distributed storage"
    - "repairing multiple failures with coordinated and adaptive regenerating codes"
    - "cost bandwidth tradeoff in distributed storage systems"

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.IT(Information Theory)