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: "minimizing communication in linear algebra"
  Abstract: In 1981 Hong and Kung proved a lower bound on the amount of communication (amount of data moved between a small, fast memory and large, slow memory) needed to perform dense, n-by-n matrix multiplication using the conventional O(n3) algorithm, where the input matrices were too large to fit in the small, fast memory. In 2004 Irony, Toledo, and Tiskin gave a new proof of this result and extended it to the parallel case (where communication means the amount of data moved between processors). In both cases the lower bound may be expressed as Ω(#arithmetic_operations/M), where M is the size of the fast memory (or local memory in the parallel case). Here we generalize these results to a much wider variety of algorithms, including LU factorization, Cholesky factorization, LDLT factorization, QR factorization, the Gram–Schmidt algorithm, and algorithms for eigenvalues and singular values, i.e., essentially all direct methods of linear algebra. The proof works for dense or sparse matrices and for sequential or para...

Connected through hyperedge HE-1 (induced by source paper "communication efficient jaccard similarity for high performance distributed genome comparisons"):
  with members:
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "maximally consistent sampling and the jaccard index of probability distributions"
    - "a note on the triangle inequality for the jaccard distance"
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"
    - "mathematical foundations of the graphblas"
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"
    - "sparse tensor algebra as a parallel programming model"
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"

Connected through hyperedge HE-2 (induced by source paper "the i o complexity of hybrid algorithms for square matrix multiplication"):
  with members:
    - "the i o complexity of strassen s matrix multiplication with recomputation"
    - "powers of tensors and fast matrix multiplication"
    - "communication lower bounds for distributed memory computations"
    - "the input output complexity of sparse matrix multiplication"
    - "communication lower bounds for distributed memory computations"
    - "graph expansion analysis for communication costs of fast rectangular matrix multiplication"
    - "communication optimal parallel and sequential cholesky decomposition"

Connected through hyperedge HE-3 (induced by source paper "improving the space time efficiency of processor oblivious matrix multiplication algorithms"):
  with members:
    - "extending the nested parallel model to the nested dataflow model with provably efficient schedulers"
    - "implementing strassen s algorithm with blis"
    - "a framework for practical parallel fast matrix multiplication"
    - "communication optimal parallel algorithm for strassen s matrix multiplication"
    - "memory efficient scheduling of strassen winograd s matrix multiplication algorithm"

Connected through hyperedge HE-4 (induced by source paper "automated derivation of parametric data movement lower bounds for affine programs"):
  with members:
    - "pushing the bounds for matrix matrix multiplication"
    - "on characterizing the data movement complexity of computational dags for parallel execution"
    - "revisiting matrix product on master worker platforms"
    - "introduction to the ginac framework for symbolic computation within the c programming language"

Connected through hyperedge HE-5 (induced by source paper "improved parallel cache oblivious algorithms for dynamic programming and linear algebra"):
  with members:
    - "persistent memory i o primitives"
    - "parallel write efficient algorithms and data structures for computational geometry"
    - "algorithmic building blocks for asymmetric memories"
    - "the parallel persistent memory model"
    - "resource oblivious sorting on multicores"
    - "implicit decomposition for write efficient connectivity algorithms"
    - "on the fine grained complexity of one dimensional dynamic programming"
    - "extending the nested parallel model to the nested dataflow model with provably efficient schedulers"

Connected through hyperedge HE-6 (induced by source paper "mesh tensorflow deep learning for supercomputers"):
  with members:
    - "beyond data and model parallelism for deep neural networks"
    - "communication optimal convolutional neural nets"
    - "exploring hidden dimensions in parallelizing convolutional neural networks"
    - "integrated model batch and domain parallelism in training neural networks"
    - "attention is all you need"
    - "exploring the limits of language modeling"
    - "scalable task based algorithm for multiplication of block rank sparse matrices"

Connected through hyperedge HE-7 (induced by source paper "numpywren serverless linear algebra"):
  with members:
    - "ray a distributed framework for emerging ai applications"
    - "occupy the cloud distributed computing for the 99"
    - "mllib machine learning in apache spark"
    - "communication optimal parallel and sequential cholesky decomposition"

Connected through hyperedge HE-8 (induced by source paper "memcomputing numerical inversion with self organizing logic gates"):
  with members:
    - "universal memcomputing machines"
    - "deep learning in neural networks"
    - "memcomputing np complete problems in polynomial time using polynomial resources and collective states"
    - "the parallel approach"

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.CC(Computational Complexity)