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 "on the i o complexity of the k nearest neighbor problem"):
  Abstract: We consider static, external memory indexes for exact and approximate versions of the $k$-nearest neighbor ($k$-NN) problem, and show new lower bounds under a standard indivisibility assumption: #R##N#- Polynomial space indexing schemes for high-dimensional $k$-NN in Hamming space cannot take advantage of block transfers: $\Omega(k)$ block reads are needed to to answer a query. #R##N#- For the $\ell_\infty$ metric the lower bound holds even if we allow $c$-appoximate nearest neighbors to be returned, for $c \in (1, 3)$. #R##N#- The restriction to $c < 3$ is necessary: For every metric there exists an indexing scheme in the indexability model of Hellerstein et al.~using space $O(kn)$, where $n$ is the number of points, that can retrieve $k$ 3-approximate nearest neighbors using $\lceil k/B\rceil$ I/Os, which is optimal. #R##N#- For specific metrics, data structures with better approximation factors are possible. For $k$-NN in Hamming space and every approximation factor $c>1$ there exists a polynomial space data structure that returns $k$ $c$-approximate nearest neighbors in $\lceil k/B\rceil$ I/Os. #R##N#To show these lower bounds we develop two new techniques: First, to handle that approximation algorithms have more freedom in deciding which result set to return we develop a relaxed version of the $\lambda$-set workload technique of Hellerstein et al. This technique allows us to show lower bounds that hold in $d\geq n$ dimensions. To extend the lower bounds down to $d = O(k \log(n/k))$ dimensions, we develop a new deterministic dimension reduction technique that may be of independent interest.

Includes members:
  Member "approximate nearest neighbor search in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "copt coordinated optimal transport on graphs")
  Member "hardness of approximate nearest neighbor search":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "algorithms for similarity search and pseudorandomness")
  Member "optimal hashing based time space trade offs for approximate near neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximating text to pattern hamming distances")
  Member "neural codes for image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")
  Member "efficient distributed locality sensitive hashing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning space partitions for nearest neighbor search")
  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")
  Member "lower bounds on performance of metric tree indexing schemes for exact similarity search in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "off the beaten path let s replace term based retrieval with k nn search")
  Member "dynamic external hashing the limit of buffering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "data oblivious external memory algorithms for the compaction selection and sorting of outsourced data")

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)