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 "the orthogonal vectors conjecture for branching programs and formulas"):
  Abstract: In the Orthogonal Vectors (OV) problem, we wish to determine if there is an orthogonal pair of vectors among $n$ Boolean vectors in $d$ dimensions. The OV Conjecture (OVC) posits that OV requires $n^{2-o(1)}$ time to solve, for all $d=\omega(\log n)$. Assuming the OVC, optimal time lower bounds have been proved for many prominent problems in $P$. #R##N#We prove that OVC is true in several computational models of interest: #R##N#* For all sufficiently large $n$ and $d$, OV for $n$ vectors in $\{0,1\}^d$ has branching program complexity $\tilde{\Theta}(n\cdot \min(n,2^d))$. In particular, the lower bounds match the upper bounds up to polylog factors. #R##N#* OV has Boolean formula complexity $\tilde{\Theta}(n\cdot \min(n,2^d))$, over all complete bases of $O(1)$ fan-in. #R##N#* OV requires $\tilde{\Theta}(n\cdot \min(n,2^d))$ wires, in formulas comprised of gates computing arbitrary symmetric functions of unbounded fan-in. #R##N#Our lower bounds basically match the best known (quadratic) lower bounds for any explicit function in those models. Analogous lower bounds hold for many related problems shown to be hard under OVC, such as Batch Partial Match, Batch Subset Queries, and Batch Hamming Nearest Neighbors, all of which have very succinct reductions to OV. #R##N#The proofs use a certain kind of input restriction that is different from typical random restrictions where variables are assigned independently. We give a sense in which independent random restrictions cannot be used to show hardness, in that OVC is false in the "average case" even for $AC^0$ formulas: #R##N#* For every fixed $p \in (0,1)$ there is an $\epsilon_p > 0$ such that for every $n$ and $d$, OV instances where input bits are independently set to $1$ with probability $p$ (and $0$ otherwise) can be solved with $AC^0$ formulas of size $O(n^{2-\epsilon_p})$, on all but a $o_n(1)$ fraction of instances.

Includes members:
  Member "on the complexity of inner product similarity join":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lsf join locality sensitive filtering for distributed all pairs set similarity under skew")
  Member "strong eth breaks with merlin and arthur short non interactive proofs of batch evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "new algorithms and lower bounds for all pairs max flow in undirected graphs")
  Member "which regular expression patterns are hard to match":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graphs cannot be indexed in polynomial time for sub quadratic time string matching unless seth fails")
  Member "quadratic conditional lower bounds for string problems and dynamic time warping":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "streaming with oracle new streaming algorithms for edit distance and lcs")
  Member "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graphs cannot be indexed in polynomial time for sub quadratic time string matching unless seth fails")
  Member "edit distance cannot be computed in strongly subquadratic time unless seth is false":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "streaming with oracle new streaming algorithms for edit distance and lcs")

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.CC(Computational Complexity)