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: "a pliable index coding approach to data shuffling"
  Abstract: A promising research area that has recently emerged, is on how to use index coding to improve the communication efficiency in distributed computing systems, especially for data shuffling in iterative computations. In this paper, we posit that pliable index coding can offer a more efficient framework for data shuffling, as it can better leverage the many possible shuffling choices to reduce the number of transmissions. We theoretically analyze pliable index coding under data shuffling constraints, and design a hierarchical data-shuffling scheme that uses pliable coding as a component. We find benefits up to $O(ns/m)$ over index coding, where $ns/m$ is the average number of workers caching a message, and $m$, $n$, and $s$ are the numbers of messages, workers, and cache size, respectively.

Connected through hyperedge HE-1 (induced by source paper "codenet training large scale neural networks in presence of soft errors"):
  with members:
    - "accumulation bit width scaling for ultra low precision training of deep networks"
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "an application of storage optimal matdot codes for coded matrix multiplication fast k nearest neighbors estimation"
    - "hierarchical coded computation"
    - "learning a code machine learning for approximate non linear coded computation"
    - "speeding up distributed machine learning using codes"
    - "straggler resilient and communication efficient distributed iterative linear solver"
    - "training deep neural networks with 8 bit floating point numbers"

Connected through hyperedge HE-2 (induced by source paper "on the optimality of d2d coded caching with uncoded cache placement and one shot delivery"):
  with members:
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "speeding up distributed machine learning using codes"
    - "wireless mapreduce distributed computing"
    - "coded computation over heterogeneous clusters"
    - "characterizing the rate memory tradeoff in cache networks within a factor of 2"
    - "coded caching in partially cooperative d2d communication networks"
    - "how to optimally allocate resources for coded distributed computing"
    - "minimizing latency for secure distributed computing"

Connected through hyperedge HE-3 (induced by source paper "speeding up distributed machine learning using codes"):
  with members:
    - "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"
    - "on the worst case communication overhead for distributed data shuffling"
    - "information theoretic limits of data shuffling for distributed learning"
    - "efficient redundancy techniques for latency reduction in cloud systems"

Connected through hyperedge HE-4 (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-5 (induced by source paper "an application of storage optimal matdot codes for coded matrix multiplication fast k nearest neighbors estimation"):
  with members:
    - "hierarchical coded computation"
    - "learning a code machine learning for approximate non linear coded computation"
    - "speeding up distributed machine learning using codes"
    - "straggler resilient and communication efficient distributed iterative linear solver"
    - "coded fft and its communication overhead"
    - "a unified coded deep neural network training strategy based on generalized polydot codes"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "rateless codes for near perfect load balancing in distributed matrix vector multiplication"

Connected through hyperedge HE-6 (induced by source paper "a unified coded deep neural network training strategy based on generalized polydot codes"):
  with members:
    - "an application of storage optimal matdot codes for coded matrix multiplication fast k nearest neighbors estimation"
    - "hierarchical coded computation"
    - "learning a code machine learning for approximate non linear coded computation"
    - "speeding up distributed machine learning using codes"
    - "straggler resilient and communication efficient distributed iterative linear solver"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "rateless codes for near perfect load balancing in distributed matrix vector multiplication"
    - "coded sparse matrix multiplication"

Connected through hyperedge HE-7 (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"
    - "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"
    - "coded convolution for parallel and distributed computing within a deadline"

Connected through hyperedge HE-8 (induced by source paper "compressed coded distributed computing"):
  with members:
    - "leveraging coding techniques for speeding up distributed computing"
    - "speeding up distributed machine learning using codes"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "coding for distributed fog computing"
    - "communication vs distributed computation an alternative trade off curve"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"

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)