human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "on problems equivalent to min convolution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the fine grained complexity of parity problems")
  Member "more consequences of falsifying seth and the orthogonal vectors conjecture":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "coarse grained complexity for dynamic algorithms")
  Member "hardness of approximate nearest neighbor search":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the i o complexity of the k nearest neighbor problem")
  Member "on the hardness of approximate and exact bichromatic maximum inner product":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating subset sum is equivalent to min plus convolution")
  Member "tight hardness for shortest cycles and paths in sparse graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "new algorithms and hardness for incremental single source shortest paths in directed graphs")
  Member "tight hardness results for maximum weight rectangles":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the fine grained complexity of parity problems")
  Member "on the complexity of inner product similarity join":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lsf join locality sensitive filtering for distributed all pairs set similarity under skew")
  Member "polynomial representations of threshold functions and algorithmic applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximating text to pattern hamming distances")

Question: Please tell me which class the source paper 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)