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: "a framework for practical parallel fast matrix multiplication"
  Abstract: Matrix multiplication is a fundamental computation in many scientific disciplines. In this paper, we show that novel fast matrix multiplication algorithms can significantly outperform vendor implementations of the classical algorithm and Strassen's fast algorithm on modest problem sizes and shapes. Furthermore, we show that the best choice of fast algorithm depends not only on the size of the matrices but also the shape. We develop a code generation tool to automatically implement multiple sequential and shared-memory parallel variants of each fast algorithm, including our novel parallelization scheme. This allows us to rapidly benchmark over 20 fast algorithms on several problem sizes. Furthermore, we discuss a number of practical implementation issues for these algorithms on shared-memory machines that can direct further research on making fast algorithms practical.

Connected through hyperedge HE-1 (induced by source paper "deterministic combinatorial replacement paths and distance sensitivity oracles"):
  with members:
    - "a deterministic distributed algorithm for exact weighted all pairs shortest paths in tilde o n 3 2 rounds"
    - "dynamic matching reducing integral algorithms to approximately maximal fractional algorithms"
    - "generating families of practical fast matrix multiplication algorithms"
    - "improved algorithms for decremental single source reachability on directed graphs"
    - "decremental single source shortest paths on undirected graphs in near linear total update time"
    - "sublinear time decremental algorithms for single source reachability and shortest paths on directed graphs"
    - "replacement paths via row minima of concise matrices"

Connected through hyperedge HE-2 (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"
    - "powers of tensors and fast matrix multiplication"
    - "communication optimal parallel algorithm for strassen s matrix multiplication"

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"
    - "communication optimal parallel algorithm for strassen s matrix multiplication"
    - "minimizing communication in linear algebra"
    - "memory efficient scheduling of strassen winograd s matrix multiplication algorithm"

Connected through hyperedge HE-4 (induced by source paper "randomization of approximate bilinear computation for matrix multiplication"):
  with members:
    - "training deep neural networks with 8 bit floating point numbers"
    - "implementing strassen s algorithm with cutlass on nvidia volta gpus"
    - "improving the numerical stability of fast matrix multiplication"
    - "deep learning with limited numerical precision"
    - "compressed matrix multiplication"

Connected through hyperedge HE-5 (induced by source paper "equivalent polyadic decompositions of matrix multiplication tensors"):
  with members:
    - "improving the numerical stability of fast matrix multiplication"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-6 (induced by source paper "fast strassen based a t a parallel multiplication"):
  with members:
    - "powers of tensors and fast matrix multiplication"
    - "communication optimal parallel algorithm for strassen s matrix multiplication"

Connected through hyperedge HE-7 (induced by source paper "fast feasible and unfeasible matrix multiplication"):
  with members:
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"
    - "an improved combinatorial algorithm for boolean matrix multiplication"
    - "improving the numerical stability of fast matrix multiplication algorithms"
    - "fast matrix multiplication limitations of the laser method"
    - "powers of tensors and fast matrix multiplication"
    - "nearly optimal computations with structured matrices"
    - "better late than never filling a void in the history of fast matrix multiplication and tensor decompositions"

Connected through hyperedge HE-8 (induced by source paper "a new algorithm for finding closest pair of vectors"):
  with members:
    - "an illuminating algorithm for the light bulb problem"
    - "fast feasible and unfeasible matrix multiplication"
    - "distributed pcp theorems for hardness of approximation in p"
    - "generating families of practical fast matrix multiplication algorithms"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time"
    - "a faster subquadratic algorithm for finding outlier correlations"
    - "probabilistic polynomials and hamming nearest neighbors"

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)