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: "on the worst case communication overhead for distributed data shuffling"
  Abstract: Distributed learning platforms for processing large scale data-sets are becoming increasingly prevalent. In typical distributed implementations, a centralized master node breaks the data-set into smaller batches for parallel processing across distributed workers to achieve speed-up and efficiency. Several computational tasks are of sequential nature, and involve multiple passes over the data. At each iteration over the data, it is common practice to randomly re-shuffle the data at the master node, assigning different batches for each worker to process. This random re-shuffling operation comes at the cost of extra communication overhead, since at each shuffle, new data points need to be delivered to the distributed workers. #R##N#In this paper, we focus on characterizing the information theoretically optimal communication overhead for the distributed data shuffling problem. We propose a novel coded data delivery scheme for the case of no excess storage, where every worker can only store the assigned data batches under processing. Our scheme exploits a new type of coding opportunity and is applicable to any arbitrary shuffle, and for any number of workers. We also present an information theoretic lower bound on the minimum communication overhead for data shuffling, and show that the proposed scheme matches this lower bound for the worst-case communication overhead.

Connected through hyperedge HE-1 (induced by source paper "speeding up distributed machine learning using codes"):
  with members:
    - "a pliable index coding approach to data shuffling"
    - "coded computation over heterogeneous clusters"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "minimizing latency for secure distributed computing"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "a unified coding framework for distributed computing with straggling servers"
    - "information theoretic limits of data shuffling for distributed learning"
    - "efficient redundancy techniques for latency reduction in cloud systems"

Connected through hyperedge HE-2 (induced by source paper "wireless mapreduce distributed computing"):
  with members:
    - "leveraging coding techniques for speeding up distributed computing"
    - "speeding up distributed machine learning using codes"
    - "federated learning strategies for improving communication efficiency"
    - "distributed computing with heterogeneous communication constraints the worst case computation load and proof by contradiction"
    - "coded computing for distributed graph analytics"
    - "hierarchical coding for distributed computing"
    - "compressed coded distributed computing"
    - "coded distributed computing with node cooperation substantially increases speedup factors"

Connected through hyperedge HE-3 (induced by source paper "near optimal coded data shuffling for distributed learning"):
  with members:
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "speeding up distributed machine learning using codes"
    - "on the fundamental limits of coded data shuffling for distributed learning systems"
    - "a pliable index coding approach to data shuffling"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "low subpacketization schemes for coded caching"
    - "latency analysis of coded computation schemes over wireless networks"
    - "communication vs distributed computation an alternative trade off curve"

Connected through hyperedge HE-4 (induced by source paper "fundamental limits of decentralized data shuffling"):
  with members:
    - "coded distributed computing with heterogeneous function assignments"
    - "cascaded coded distributed computing on heterogeneous networks"
    - "embedded index coding"
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "leveraging coding techniques for speeding up distributed computing"
    - "speeding up distributed machine learning using codes"
    - "storage computation and communication a fundamental tradeoff in distributed computing"
    - "coded computing for distributed graph analytics"

Connected through hyperedge HE-5 (induced by source paper "storage computation and communication a fundamental tradeoff in distributed computing"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "hierarchical coding for distributed computing"
    - "placement delivery array design through strong edge coloring of bipartite graphs"
    - "a storage computation communication tradeoff for distributed computing"
    - "coded distributed computing with node cooperation substantially increases speedup factors"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "communication vs distributed computation an alternative trade off curve"

Connected through hyperedge HE-6 (induced by source paper "on the fundamental limits of coded data shuffling for distributed learning systems"):
  with members:
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "speeding up distributed machine learning using codes"
    - "near optimal coded data shuffling for distributed learning"
    - "a pliable index coding approach to data shuffling"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "a unified coding framework for distributed computing with straggling servers"
    - "fundamental limits of caching"

Connected through hyperedge HE-7 (induced by source paper "distributed computing with heterogeneous communication constraints the worst case computation load and proof by contradiction"):
  with members:
    - "latency analysis of coded computation schemes over wireless networks"
    - "coded convolution for parallel and distributed computing within a deadline"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "how to optimally allocate resources for coded distributed computing"
    - "on heterogeneous coded distributed computing"

Connected through hyperedge HE-8 (induced by source paper "coding method for parallel iterative linear solver"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "coded computation over heterogeneous clusters"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "coding for distributed fog computing"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "analog coding of a source with erasures"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"

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)