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: "communication optimal parallel algorithm for strassen s matrix multiplication"
  Abstract: Parallel matrix multiplication is one of the most studied fundamental problems in distributed and high performance computing. We obtain a new parallel algorithm that is based on Strassen's fast matrix multiplication and minimizes communication. The algorithm outperforms all known parallel matrix multiplication algorithms, classical and Strassen-based, both asymptotically and in practice. #R##N#A critical bottleneck in parallelizing Strassen's algorithm is the communication between the processors. Ballard, Demmel, Holtz, and Schwartz (SPAA'11) prove lower bounds on these communication costs, using expansion properties of the underlying computation graph. Our algorithm matches these lower bounds, and so is communication-optimal. It exhibits perfect strong scaling within the maximum possible range. #R##N#Benchmarking our implementation on a Cray XT4, we obtain speedups over classical and Strassen-based algorithms ranging from 24% to 184% for a fixed matrix dimension n=94080, where the number of nodes ranges from 49 to 7203. #R##N#Our parallelization approach generalizes to other fast matrix multiplication algorithms.

Connected through hyperedge HE-1 (induced by source paper "engineering boolean matrix multiplication for multiple accelerator shared memory architectures"):
  with members:
    - "fast feasible and unfeasible matrix multiplication"
    - "generating families of practical fast matrix multiplication algorithms"
    - "dissecting gpu memory hierarchy through microbenchmarking"
    - "an improved combinatorial algorithm for boolean matrix multiplication"
    - "a framework for practical parallel fast matrix multiplication"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-2 (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"
    - "minimizing communication in linear algebra"
    - "memory efficient scheduling of strassen winograd s matrix multiplication algorithm"

Connected through hyperedge HE-3 (induced by source paper "fast strassen based a t a parallel multiplication"):
  with members:
    - "a framework for practical parallel fast matrix multiplication"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-4 (induced by source paper "stark fast and scalable strassen s matrix multiplication using apache spark"):
  with members:
    - "mllib machine learning in apache spark"

Connected through hyperedge HE-5 (induced by source paper "design configuration implementation and performance of a simple 32 core raspberry pi cluster"):
  with members:
    - "variable annealing length and parallelism in simulated annealing"

Connected through hyperedge HE-6 (induced by source paper "the i o complexity of strassen s matrix multiplication with recomputation"):
  with members:
    - "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"
    - "faster algorithms for rectangular matrix multiplication"
    - "minimizing communication in linear algebra"
    - "communication optimal parallel and sequential cholesky decomposition"

Connected through hyperedge HE-7 (induced by source paper "generating families of practical fast matrix multiplication algorithms"):
  with members:
    - "the i o complexity of strassen s matrix multiplication with recomputation"
    - "improving the numerical stability of fast matrix multiplication"
    - "a framework for practical parallel fast matrix multiplication"

Connected through hyperedge HE-8 (induced by source paper "algebraic methods in the congested clique"):
  with members:
    - "approximation of distances and shortest paths in the broadcast congest clique"
    - "minimum weight spanning tree construction in o log log log n rounds on the congested clique"
    - "powers of tensors and fast matrix multiplication"
    - "distributed approximation algorithms for weighted shortest paths"
    - "lessons from the congested clique applied to mapreduce"
    - "near constant time distributed algorithms on a congested clique"
    - "faster all pairs shortest paths via circuit complexity"
    - "efficient computation of representative sets with applications in parameterized and exact algorithms"

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.DS(Data Structures and Algorithms)