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: "the input output complexity of sparse matrix multiplication"
  Abstract: We consider the problem of multiplying sparse matrices (over a semiring) where the number of non-zero entries is larger than main memory. In the classical paper of Hong and Kung (STOC '81) it was shown that to compute a product of dense $U \times U$ matrices, $\Theta \left(U^3 / (B \sqrt{M}) \right)$ I/Os are necessary and sufficient in the I/O model with internal memory size $M$ and memory block size $B$. #R##N#In this paper we generalize the upper and lower bounds of Hong and Kung to the sparse case. Our bounds depend of the number $N = \mathtt{nnz}(A)+\mathtt{nnz}(C)$ of nonzero entries in $A$ and $C$, as well as the number $Z = \mathtt{nnz}(AC)$ of nonzero entries in $AC$. #R##N#We show that $AC$ can be computed using $\tilde{O} \left(\tfrac{N}{B} \min\left(\sqrt{\tfrac{Z}{M}},\tfrac{N}{M}\right) \right)$ I/Os, with high probability. This is tight (up to polylogarithmic factors) when only semiring operations are allowed, even for dense rectangular matrices: We show a lower bound of $\Omega \left(\tfrac{N}{B} \min\left(\sqrt{\tfrac{Z}{M}},\tfrac{N}{M}\right) \right)$ I/Os. #R##N#While our lower bound uses fairly standard techniques, the upper bound makes use of ``compressed matrix multiplication'' sketches, which is new in the context of I/O-efficient algorithms, and a new matrix product size estimation technique that avoids the ``no cancellation'' assumption.

Connected through hyperedge HE-1 (induced by source paper "enabling distributed memory tensor completion in python using new sparse tensor kernels"):
  with members:
    - "pasta a parallel sparse tensor algorithm benchmark suite"
    - "format abstraction for sparse tensor algebracompilers"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "shared memory parallelization of mttkrp for dense tensors"
    - "communication lower bounds for matricized tensor times khatri rao product"
    - "hptt a high performance tensor transposition c library"
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"
    - "tensorflow a system for large scale machine learning"

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"
    - "communication lower bounds for distributed memory computations"
    - "graph expansion analysis for communication costs of fast rectangular matrix multiplication"
    - "minimizing communication in linear algebra"
    - "communication optimal parallel and sequential cholesky decomposition"

Connected through hyperedge HE-3 (induced by source paper "fine grained i o complexity via reductions new lower bounds faster algorithms and a time hierarchy"):
  with members:
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "the input output complexity of triangle enumeration"
    - "powers of tensors and fast matrix multiplication"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"

Connected through hyperedge HE-4 (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"
    - "communication lower bounds for distributed memory computations"
    - "graph expansion analysis for communication costs of fast rectangular matrix multiplication"
    - "faster algorithms for rectangular matrix multiplication"
    - "communication optimal parallel algorithm for strassen s matrix multiplication"
    - "minimizing communication in linear algebra"
    - "communication optimal parallel and sequential cholesky decomposition"

Connected through hyperedge HE-5 (induced by source paper "hypergraph partitioning for sparse matrix matrix multiplication"):
  with members:
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"
    - "hypergraph partitioning based models and methods for exploiting cache locality in sparse matrix vector multiplication"
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"
    - "minimizing communication in linear algebra"

Connected through hyperedge HE-6 (induced by source paper "a framework for general sparse matrix matrix multiplication on gpus and heterogeneous processors"):
  with members:
    - "speculative segmented sum for sparse matrix vector multiplication on heterogeneous processors"
    - "csr5 an efficient storage format for cross platform sparse matrix vector multiplication"
    - "performance evaluation of sparse matrix multiplication kernels on intel xeon phi"
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"
    - "better size estimation for sparse matrix products"
    - "finding heaviest h subgraphs in real weighted graphs with applications"

Connected through hyperedge HE-7 (induced by source paper "experimental evaluation of multi round matrix multiplication on mapreduce"):
  with members:
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"
    - "space round tradeoffs for mapreduce computations"
    - "sorting searching and simulation in the mapreduce framework"
    - "data mining using high performance data clouds experimental studies using sector and sphere"

Connected through hyperedge HE-8 (induced by source paper "the input output complexity of triangle enumeration"):
  with members:
    - "subgraph enumeration in massive graphs"
    - "why do simple algorithms for triangle enumeration work in the real world"
    - "3sum 3xor triangles"
    - "enumerating subgraph instances using map reduce"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"

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)