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: "gradient coding from cyclic mds codes and expander graphs"
  Abstract: Gradient coding is a technique for straggler mitigation in distributed learning. In this paper we design novel gradient codes using tools from classical coding theory, namely, cyclic MDS codes, which compare favorably with existing solutions, both in the applicable range of parameters and in the complexity of the involved algorithms. Second, we introduce an approximate variant of the gradient coding problem, in which we settle for approximate gradient computation instead of the exact one. This approach enables graceful degradation, i.e., the $\ell_2$ error of the approximate gradient is a decreasing function of the number of stragglers. Our main result is that normalized adjacency matrices of expander graphs yield excellent approximate gradient codes, which enable significantly less computation compared to exact gradient coding, and guarantee faster convergence than trivial solutions under standard assumptions. We experimentally test our approach on Amazon EC2, and show that the generalization error of approximate gradient coding is very close to the full gradient while requiring significantly less computation from the workers.

Connected through hyperedge HE-1 (induced by source paper "a locality based approach for coded computation"):
  with members:
    - "entangled polynomial codes for secure private and batch distributed matrix multiplication breaking the cubic barrier"
    - "slack squeeze coded computing for adaptive straggler mitigation"
    - "timely throughput optimal coded computing over cloud networks"
    - "train where the data is a case for bandwidth efficient coded training"
    - "collage inference tolerating stragglers in distributed neural network inference using coding"
    - "erasurehead distributed gradient descent without delays using approximate gradient coding"
    - "cross subspace alignment codes for coded distributed batch computation"
    - "hierarchical coded computation"

Connected through hyperedge HE-2 (induced by source paper "numerically stable binary gradient coding"):
  with members:
    - "fundamental limits of approximate gradient coding"
    - "coded distributed computing performance limits and code designs"
    - "gradient coding based on block designs for mitigating adversarial stragglers"
    - "gradient coding with clustering and multi message communication"
    - "distributed stochastic gradient descent using ldgm codes"
    - "universally decodable matrices for distributed matrix vector multiplication"
    - "erasurehead distributed gradient descent without delays using approximate gradient coding"
    - "speeding up distributed machine learning using codes"

Connected through hyperedge HE-3 (induced by source paper "adaptive distributed stochastic gradient descent for minimizing delay in the presence of stragglers"):
  with members:
    - "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"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "exploitation of stragglers in coded computation"
    - "don t decay the learning rate increase the batch size"
    - "short dot computing large linear transforms distributedly using coded short dot products"

Connected through hyperedge HE-4 (induced by source paper "weighted gradient coding with leverage score sampling"):
  with members:
    - "numerically stable binary gradient coding"
    - "fundamental limits of approximate gradient coding"
    - "gradient coding based on block designs for mitigating adversarial stragglers"
    - "gradient coding with clustering and multi message communication"
    - "distributed stochastic gradient descent using ldgm codes"
    - "erasurehead distributed gradient descent without delays using approximate gradient coding"
    - "balanced reed solomon codes"
    - "lecture notes on randomized linear algebra"

Connected through hyperedge HE-5 (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-6 (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-7 (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-8 (induced by source paper "election coding for distributed learning protecting signsgd against byzantine attacks"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "detox a redundancy based framework for faster and more robust gradient aggregation"
    - "fast and secure distributed learning in high dimension"
    - "byzantine stochastic gradient descent"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "speeding up distributed machine learning using codes"
    - "federated learning strategies for improving communication efficiency"

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)