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: "coded fourier transform"
  Abstract: We consider the problem of computing the Fourier transform of high-dimensional vectors, distributedly over a cluster of machines consisting of a master node and multiple worker nodes, where the worker nodes can only store and process a fraction of the inputs. We show that by exploiting the algebraic structure of the Fourier transform operation and leveraging concepts from coding theory, one can efficiently deal with the straggler effects. In particular, we propose a computation strategy, named as coded FFT, which achieves the optimal recovery threshold, defined as the minimum number of workers that the master node needs to wait for in order to compute the output. This is the first code that achieves the optimum robustness in terms of tolerating stragglers or failures for computing Fourier transforms. Furthermore, the reconstruction process for coded FFT can be mapped to MDS decoding, which can be solved efficiently. Moreover, we extend coded FFT to settings including computing general $n$-dimensional Fourier transforms, and provide the optimal computing strategy for those settings.

Connected through hyperedge HE-1 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication"):
  with members:
    - "codedprivateml a fast and privacy preserving framework for distributed machine learning"
    - "x secure t private information retrieval from mds coded storage with byzantine and unresponsive servers"
    - "distributed and private coded matrix computation with flexible communication load"
    - "secure coded multi party computation for massive matrix operations"
    - "on the asymptotic capacity of x secure t private information retrieval with graph based replicated storage"
    - "private secure coded computation"
    - "coded matrix multiplication on a group based model"
    - "cross subspace alignment codes for coded distributed batch computation"

Connected through hyperedge HE-2 (induced by source paper "cross subspace alignment codes for coded distributed batch computation"):
  with members:
    - "x secure t private information retrieval from mds coded storage with byzantine and unresponsive servers"
    - "on the capacity of secure distributed matrix multiplication"
    - "on the asymptotic capacity of x secure t private information retrieval with graph based replicated storage"
    - "coded matrix multiplication on a group based model"
    - "an application of storage optimal matdot codes for coded matrix multiplication fast k nearest neighbors estimation"
    - "speeding up distributed machine learning using codes"
    - "rate efficiency and straggler robustness through partition in distributed two sided secure matrix computation"
    - "cross subspace alignment and the asymptotic capacity of x secure t private information retrieval"

Connected through hyperedge HE-3 (induced by source paper "coded distributed computing over packet erasure channels"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "hierarchical coding for distributed computing"
    - "exploitation of stragglers in coded computation"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "latency analysis of coded computation schemes over wireless networks"
    - "coded convolution for parallel and distributed computing within a deadline"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "information theoretic limits of data shuffling for distributed learning"

Connected through hyperedge HE-4 (induced by source paper "coded matrix multiplication on a group based model"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "hierarchical coding for distributed computing"
    - "coded computation over heterogeneous clusters"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "coded convolution for parallel and distributed computing within a deadline"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "gradient coding from cyclic mds codes and expander graphs"
    - "improving distributed gradient descent using reed solomon codes"

Connected through hyperedge HE-5 (induced by source paper "array bp xor codes for parallel matrix multiplication using hierarchical computing"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "coded convolution for parallel and distributed computing within a deadline"
    - "asymptotically mds array bp xor codes"

Connected through hyperedge HE-6 (induced by source paper "on the optimal recovery threshold of coded matrix multiplication"):
  with members:
    - "codenet training large scale neural networks in presence of soft errors"
    - "an application of storage optimal matdot codes for coded matrix multiplication fast k nearest neighbors estimation"
    - "speeding up distributed machine learning using codes"
    - "fundamental limits of coded linear transform"
    - "a unified coded deep neural network training strategy based on generalized polydot codes"
    - "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 "rateless codes for near perfect load balancing in distributed matrix vector multiplication"):
  with members:
    - "parity models a general framework for coding based resilience in ml inference"
    - "learning a code machine learning for approximate non linear coded computation"
    - "speeding up distributed machine learning using codes"
    - "numpywren serverless linear algebra"
    - "oversketch approximate matrix multiplication for the cloud"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "coded sparse matrix multiplication"
    - "near optimal straggler mitigation for distributed gradient methods"

Connected through hyperedge HE-8 (induced by source paper "coded fft and its communication overhead"):
  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"
    - "coded convolution for parallel and distributed computing within a deadline"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "block diagonal and lt codes for distributed computing with straggling servers"
    - "a unified coding framework for distributed computing with straggling servers"
    - "a decomposition method with minimum communication amount for parallelization of multi dimensional ffts"

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.DC(Distributed, Parallel, and Cluster Computing)