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: "hamming distance completeness and sparse matrix multiplication"
  Abstract: We investigate relations between $(+,\diamond)$ vector products for binary integer functions $\diamond$. We show that there exists a broad class of products equivalent under one-to-polylog reductions to the computation of the Hamming distance. Examples include: the dominance product, the threshold product and $\ell_{2p+1}$ distances for constant $p$. Our result has the following consequences: #R##N#The following All Pairs- problems are of the same complexity (up to polylog factors) for $n$ vectors in $Z^d$: computing Hamming Distance, $\ell_{2p+1}$ Distance, Threshold Products and Dominance Products. As a consequence, Yuster's~(SODA'09) algorithm improves not only Matou\v{s}ek's (IPL'91), but also the results of Indyk, Lewenstein, Lipsky and Porat (ICALP'04) and Min, Kao and Zhu (COCOON'09). Obtain by reduction algorithms for All Pairs $\ell_3,\ell_5,\dots$ Distances are new. #R##N#The following Pattern Matching problems are of the same complexity (up to polylog factors) for a text of length $n$ and a pattern of length $m$: Hamming Distance, Less-than, Threshold and $\ell_{2p+1}$. For all of them the current best upper bounds are $\mathcal{O}(n\sqrt{m \log m})$ time due to results of Abrahamson (SICOMP'87), Amir and Farach (Ann.~Math.~Artif.~Intell.'91), Atallah and Duket (IPL'11), Clifford, Clifford and Iliopoulous (CPM'05) and Amir, Lipsky, Porat and Umanski (CPM'05). The obtained algorithms for $\ell_3,\ell_5,\dots$ Pattern Matchings are new. #R##N#We also show that the complexity of All Pairs Hamming Distances is within a polylog factor from $sparse(n,d^2,n;nd,nd)$, where $sparse(a,b,c;m_1,m_2)$ is the time of multiplying sparse matrices of size $a\times b$ and $b\times c$, with $m_1$ and $m_2$ nonzero entries. This means that the current upperbounds by Yuster cannot be improved without improving the sparse matrix multiplication algorithm by Yuster and Zwick~(ACM TALG'05) and vice versa.

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)