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 "randomized approximate nearest neighbor search with limited adaptivity"):
  Abstract: We study the fundamental problem of approximate nearest neighbor search in $d$-dimensional Hamming space $\{0,1\}^d$. We study the complexity of the problem in the famous cell-probe model, a classic model for data structures. We consider algorithms in the cell-probe model with limited adaptivity, where the algorithm makes $k$ rounds of parallel accesses to the data structure for a given $k$. For any $k\ge 1$, we give a simple randomized algorithm solving the approximate nearest neighbor search using $k$ rounds of parallel memory accesses, with $O(k(\log d)^{1/k})$ accesses in total. We also give a more sophisticated randomized algorithm using $O(k+(\frac{1}{k}\log d)^{O(1/k)})$ memory accesses in $k$ rounds for large enough $k$. Both algorithms use data structures of size polynomial in $n$, the number of points in the database. #R##N#For the lower bound, we prove an $\Omega(\frac{1}{k}(\log d)^{1/k})$ lower bound for the total number of memory accesses required by any randomized algorithm solving the approximate nearest neighbor search within $k\le\frac{\log\log d}{2\log\log\log d}$ rounds of parallel memory accesses on any data structures of polynomial size. This lower bound shows that our first algorithm is asymptotically optimal for any constant round $k$. And our second algorithm approaches the asymptotically optimal tradeoff between rounds and memory accesses, in a sense that the lower bound of memory accesses for any $k_1$ rounds can be matched by the algorithm within $k_2=O(k_1)$ rounds. In the extreme, for some large enough $k=\Theta\left(\frac{\log\log d}{\log\log\log d}\right)$, our second algorithm matches the $\Theta\left(\frac{\log\log d}{\log\log\log d}\right)$ tight bound for fully adaptive algorithms for approximate nearest neighbor search due to Chakrabarti and Regev.

Includes members:
  Member "simple average case lower bounds for approximate near neighbor from isoperimetric inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lower bounds on time space trade offs for approximate near neighbors")
  Member "optimal data dependent hashing for approximate near neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "autoblock a hands off blocking framework for entity matching")
  Member "certificates in data structures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "coarse grained complexity for dynamic algorithms")
  Member "beyond locality sensitive hashing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dynamic streaming spectral sparsification in nearly linear time and space")
  Member "adapt or die polynomial lower bounds for non adaptive dynamic data structures":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "equivalence of systematic linear data structures and matrix rigidity")
  Member "lower bounds on near neighbor search via metric expansion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "tight static lower bounds for non adaptive data structures")

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)