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: "deterministic algorithms for skewed matrix products"
  Abstract: Recently, Pagh presented a randomized approximation algorithm for the multiplication of real-valued matrices building upon work for detecting the most frequent items in data streams. We continue this line of research and present new {\em deterministic} matrix multiplication algorithms. #R##N#Motivated by applications in data mining, we first consider the case of real-valued, nonnegative $n$-by-$n$ input matrices $A$ and $B$, and show how to obtain a deterministic approximation of the weights of individual entries, as well as the entrywise $p$-norm, of the product $AB$. The algorithm is simple, space efficient and runs in one pass over the input matrices. For a user defined $b \in (0, n^2)$ the algorithm runs in time $O(nb + n\cdot\text{Sort}(n))$ and space $O(n + b)$ and returns an approximation of the entries of $AB$ within an additive factor of $\|AB\|_{E1}/b$, where $\|C\|_{E1} = \sum_{i, j} |C_{ij}|$ is the entrywise 1-norm of a matrix $C$ and $\text{Sort}(n)$ is the time required to sort $n$ real numbers in linear space. Building upon a result by Berinde et al. we show that for skewed matrix products (a common situation in many real-life applications) the algorithm is more efficient and achieves better approximation guarantees than previously known randomized algorithms. #R##N#When the input matrices are not restricted to nonnegative entries, we present a new deterministic group testing algorithm detecting nonzero entries in the matrix product with large absolute value. The algorithm is clearly outperformed by randomized matrix multiplication algorithms, but as a byproduct we obtain the first $O(n^{2 + \varepsilon})$-time deterministic algorithm for matrix products with $O(\sqrt{n})$ nonzero entries.

Connected through hyperedge HE-1 (induced by source paper "faster neural network training with approximate tensor operations"):
  with members:
    - "training simplification and model simplification for deep learning a minimal effort back propagation method"
    - "factorization tricks for lstm networks"
    - "mixed precision training"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "learning compact recurrent neural networks"
    - "wide residual networks"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-2 (induced by source paper "on nondeterministic derandomization of freivalds algorithm consequences avenues and algorithmic progress"):
  with members:
    - "error correction in fast matrix multiplication and inverse"
    - "near optimal linear decision trees for k sum and related problems"
    - "strong eth breaks with merlin and arthur short non interactive proofs of batch evaluation"
    - "threesomes degenerates and love triangles"
    - "powers of tensors and fast matrix multiplication"
    - "popular conjectures imply strong lower bounds for dynamic problems"
    - "3sum 3xor triangles"
    - "faster all pairs shortest paths via circuit complexity"

Connected through hyperedge HE-3 (induced by source paper "fast approximate matrix multiplication by solving linear systems"):
  with members:
    - "powers of tensors and fast matrix multiplication"
    - "compressed matrix multiplication"
    - "low rank matrix valued chernoff bounds and approximate matrix multiplication"

Connected through hyperedge HE-4 (induced by source paper "compressed matrix multiplication"):
  with members:
    - "faster algorithms for rectangular matrix multiplication"
    - "algorithmic and statistical perspectives on large scale data analysis"
    - "better size estimation for sparse matrix products"
    - "the power of simple tabulation hashing"
    - "approximate sparse recovery optimizing time and measurements"
    - "ams without 4 wise independence on product domains"

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)