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 based on block designs for mitigating adversarial stragglers"
  Abstract: Distributed implementations of gradient-based methods, wherein a server distributes gradient computations across worker machines, suffer from slow running machines, called 'stragglers'. Gradient coding is a coding-theoretic framework to mitigate stragglers by enabling the server to recover the gradient sum in the presence of stragglers. 'Approximate gradient codes' are variants of gradient codes that reduce computation and storage overhead per worker by allowing the server to approximately reconstruct the gradient sum. #R##N#In this work, our goal is to construct approximate gradient codes that are resilient to stragglers selected by a computationally unbounded adversary. Our motivation for constructing codes to mitigate adversarial stragglers stems from the challenge of tackling stragglers in massive-scale elastic and serverless systems, wherein it is difficult to statistically model stragglers. Towards this end, we propose a class of approximate gradient codes based on balanced incomplete block designs (BIBDs). We show that the approximation error for these codes depends only on the number of stragglers, and thus, adversarial straggler selection has no advantage over random selection. In addition, the proposed codes admit computationally efficient decoding at the server. Next, to characterize fundamental limits of adversarial straggling, we consider the notion of 'adversarial threshold' -- the smallest number of workers that an adversary must straggle to inflict certain approximation error. We compute a lower bound on the adversarial threshold, and show that codes based on symmetric BIBDs maximize this lower bound among a wide class of codes, making them excellent candidates for mitigating adversarial stragglers.

Connected through hyperedge HE-1 (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 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"
    - "rateless codes for near perfect load balancing in distributed matrix vector multiplication"

Connected through hyperedge HE-2 (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 with clustering and multi message communication"
    - "distributed stochastic gradient descent using ldgm codes"
    - "erasurehead distributed gradient descent without delays using approximate gradient coding"
    - "gradient coding from cyclic mds codes and expander graphs"
    - "balanced reed solomon codes"
    - "lecture notes on randomized linear algebra"

Connected through hyperedge HE-3 (induced by source paper "coded computing for boolean functions"):
  with members:
    - "codedprivateml a fast and privacy preserving framework for distributed machine learning"
    - "slack squeeze coded computing for adaptive straggler mitigation"
    - "secure coded multi party computation for massive matrix operations"
    - "timely throughput optimal coded computing over cloud networks"
    - "hierarchical coded computation"
    - "speeding up distributed machine learning using codes"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "coded computation over heterogeneous clusters"

Connected through hyperedge HE-4 (induced by source paper "lagc lazily aggregated gradient coding for straggler tolerant and communication efficient distributed learning"):
  with members:
    - "gradient coding with clustering and multi message communication"
    - "stochastic gradient coding for straggler mitigation in distributed learning"
    - "erasurehead distributed gradient descent without delays using approximate gradient coding"
    - "speeding up distributed machine learning using codes"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "gradient coding from cyclic mds codes and expander graphs"

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)