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: "faster arithmetic for number theoretic transforms"
  Abstract: We show how to improve the efficiency of the computation of fast Fourier transforms over F"p where p is a word-sized prime. Our main technique is optimisation of the basic arithmetic, in effect decreasing the total number of reductions modulo p, by making use of a redundant representation for integers modulo p. We give performance results showing a significant improvement over [email protected]?s NTL library.

Connected through hyperedge HE-1 (induced by source paper "heaan demystified accelerating fully homomorphic encryption through architecture centric analysis and optimization"):
  with members:
    - "ngraph he2 a high throughput framework for neural network inference on encrypted data"
    - "heax high performance architecture for computation on homomorphically encrypted data in the cloud"
    - "the alexnet moment for homomorphic encryption hcnn the first homomorphic cnn on encrypted data with gpus"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"

Connected through hyperedge HE-2 (induced by source paper "implementations of efficient univariate polynomial matrix algorithms and application to bivariate resultants"):
  with members:
    - "computing popov and hermite forms of rectangular polynomial matrices"
    - "fast computation of approximant bases in canonical form"
    - "block krylov techniques in the context of sparse fglm algorithms"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "fast computation of minimal interpolation bases in popov form for arbitrary shifts"
    - "fast deterministic computation of the hermite normal form and determinant of a polynomial matrix"
    - "sub quadratic decoding of one point hermitian codes"
    - "computing minimal interpolation bases"

Connected through hyperedge HE-3 (induced by source paper "faster cryptonets leveraging sparsity for real world encrypted inference"):
  with members:
    - "thinet a filter level pruning method for deep neural network compression"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "swish a self gated activation function"
    - "learning structured sparsity in deep neural networks"
    - "stealing machine learning models via prediction apis"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "a fully private pipeline for deep learning on electronic health records"):
  with members:
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "deep learning for metagenomic data using 2d embeddings and convolutional neural networks"
    - "a review of privacy and consent management in healthcare a focus on emerging data sources"
    - "identifying diabetic patients with high risk of readmission"
    - "globally optimal training of generalized polynomial neural networks with nonlinear spectral methods"

Connected through hyperedge HE-5 (induced by source paper "faster integer multiplication using plain vanilla fft primes"):
  with members:
    - "even faster integer multiplication"

Connected through hyperedge HE-6 (induced by source paper "elements of design for containers and solutions in the linbox library"):
  with members:
    - "parallel computation of echelon forms"
    - "linbox founding scope allocation parallel building blocks and separate compilation"
    - "exact sparse matrix vector multiplication on gpu s and multicore architectures"
    - "memory efficient scheduling of strassen winograd s matrix multiplication algorithm"

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.SC(Symbolic Computation)