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: "sparse matrix multiplication on an associative processor"
  Abstract: Sparse matrix multiplication is an important component of linear algebra computations. Implementing sparse matrix multiplication on an associative processor (AP) enables high level of parallelism, where a row of one matrix is multiplied in parallel with the entire second matrix, and where the execution time of vector dot product does not depend on the vector size. Four sparse matrix multiplication algorithms are explored in this paper, combining AP and baseline CPU processing to various levels. They are evaluated by simulation on a large set of sparse matrices. The computational complexity of sparse matrix multiplication on AP is shown to be an O(nnz) where nnz is the number of nonzero elements. The AP is found to be especially efficient in binary sparse matrix multiplication. AP outperforms conventional solutions in power efficiency.

Connected through hyperedge HE-1 (induced by source paper "aida associative dnn inference accelerator"):
  with members:
    - "chipmunk a systolically scalable 0 9 mm 2 3 08 gop s mw 1 2 mw accelerator for near sensor recurrent neural network inference"
    - "eie efficient inference engine on compressed deep neural network"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "speech recognition with deep recurrent neural networks"
    - "computer architecture with associative processor replacing last level cache and simd accelerator"

Connected through hyperedge HE-2 (induced by source paper "prins resistive cam processing in storage"):
  with members:
    - "application driven near data processing for similarity search"
    - "computer architecture with associative processor replacing last level cache and simd accelerator"
    - "performance evaluation of sparse matrix multiplication kernels on intel xeon phi"

Connected through hyperedge HE-3 (induced by source paper "sparse matrix multiplication on cam based accelerator"):
  with members:
    - "computer architecture with associative processor replacing last level cache and simd accelerator"
    - "performance evaluation of sparse matrix multiplication kernels on intel xeon phi"

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.MS(Mathematical Software)