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: "an equivalence class for orthogonal vectors"
  Abstract: The Orthogonal Vectors problem ($\textsf{OV}$) asks: given $n$ vectors in $\{0,1\}^{O(\log n)}$, are two of them orthogonal? $\textsf{OV}$ is easily solved in $O(n^2 \log n)$ time, and it is a central problem in fine-grained complexity: dozens of conditional lower bounds are based on the popular hypothesis that $\textsf{OV}$ cannot be solved in (say) $n^{1.99}$ time. However, unlike the APSP problem, few other problems are known to be non-trivially equivalent to $\textsf{OV}$. #R##N#We show $\textsf{OV}$ is truly-subquadratic equivalent to several fundamental problems, all of which (a priori) look harder than $\textsf{OV}$. A partial list is given below: #R##N#($\textsf{Min-IP}/\textsf{Max-IP}$) Find a red-blue pair of vectors with minimum (respectively, maximum) inner product, among $n$ vectors in $\{0,1\}^{O(\log n)}$. #R##N#($\textsf{Exact-IP}$) Find a red-blue pair of vectors with inner product equal to a given target integer, among $n$ vectors in $\{0,1\}^{O(\log n)}$. #R##N#($\textsf{Apx-Min-IP}/\textsf{Apx-Max-IP}$) Find a red-blue pair of vectors that is a 100-approximation to the minimum (resp. maximum) inner product, among $n$ vectors in $\{0,1\}^{O(\log n)}$. #R##N#(Approx. $\textsf{Bichrom.-$\ell_p$-Closest-Pair}$) Compute a $(1 + \Omega(1))$-approximation to the $\ell_p$-closest red-blue pair (for a constant $p \in [1,2]$), among $n$ points in $\mathbb{R}^d$, $d \le n^{o(1)}$. #R##N#(Approx. $\textsf{$\ell_p$-Furthest-Pair}$) Compute a $(1 + \Omega(1))$-approximation to the $\ell_p$-furthest pair (for a constant $p \in [1,2]$), among $n$ points in $\mathbb{R}^d$, $d \le n^{o(1)}$. #R##N#We also show that there is a $\text{poly}(n)$ space, $n^{1-\epsilon}$ query time data structure for Partial Match with vectors from $\{0,1\}^{O(\log n)}$ if and only if such a data structure exists for $1+\Omega(1)$ Approximate Nearest Neighbor Search in Euclidean space.

Connected through hyperedge HE-1 (induced by source paper "approximating subset sum is equivalent to min plus convolution"):
  with members:
    - "an improved fptas for 0 1 knapsack"
    - "approximating apsp without scaling equivalence of approximate min plus and exact min max"
    - "on problems equivalent to min convolution"
    - "subquadratic approximation scheme for partition"
    - "fast algorithms for knapsack via convolution and prediction"
    - "on integer programming and convolution"
    - "on the hardness of approximate and exact bichromatic maximum inner product"
    - "a simple near linear pseudopolynomial time randomized algorithm for subset sum"

Connected through hyperedge HE-2 (induced by source paper "subsets and supermajorities unifying hashing based set similarity search"):
  with members:
    - "norm ranging lsh for maximum inner product search"
    - "hardness of approximate nearest neighbor search"
    - "fast similarity sketching"
    - "distributed pcp theorems for hardness of approximation in p"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "on the complexity of inner product similarity join"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "set similarity search beyond minhash"

Connected through hyperedge HE-3 (induced by source paper "parameterized fine grained reductions"):
  with members:
    - "multivariate fine grained complexity of longest common subsequence"
    - "more consequences of falsifying seth and the orthogonal vectors conjecture"
    - "tighter connections between formula sat and shaving logs"
    - "the orthogonal vectors conjecture for branching programs and formulas"
    - "simulating branching programs with edit distance and friends or a polylog shaved is a lower bound made"
    - "approximation and fixed parameter subquadratic algorithms for radius and diameter"
    - "polynomial fixed parameter algorithms a case study for longest path on interval graphs"
    - "confronting intractability via parameters"

Connected through hyperedge HE-4 (induced by source paper "hardness of bichromatic closest pair with jaccard similarity"):
  with members:
    - "hardness of approximate nearest neighbor search"
    - "on the hardness of approximate and exact bichromatic maximum inner product"
    - "set similarity search beyond minhash"

Connected through hyperedge HE-5 (induced by source paper "on closest pair in euclidean metric monochromatic is as hard as bichromatic"):
  with members:
    - "toward super polynomial size lower bounds for depth two threshold circuits"
    - "on the hardness of approximate and exact bichromatic maximum inner product"
    - "distributed pcp theorems for hardness of approximation in p"
    - "on the complexity of closest pair via polar pair of point sets"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "powers of tensors and fast matrix multiplication"

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)