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 "tradeoffs for nearest neighbors on the sphere"):
  Abstract: We consider tradeoffs between the query and update complexities for the (approximate) nearest neighbor problem on the sphere, extending the recent spherical filters to sparse regimes and generalizing the scheme and analysis to account for different tradeoffs. In a nutshell, for the sparse regime the tradeoff between the query complexity $n^{\rho_q}$ and update complexity $n^{\rho_u}$ for data sets of size $n$ is given by the following equation in terms of the approximation factor $c$ and the exponents $\rho_q$ and $\rho_u$: $$c^2\sqrt{\rho_q}+(c^2-1)\sqrt{\rho_u}=\sqrt{2c^2-1}.$$ #R##N#For small $c=1+\epsilon$, minimizing the time for updates leads to a linear space complexity at the cost of a query time complexity $n^{1-4\epsilon^2}$. Balancing the query and update costs leads to optimal complexities $n^{1/(2c^2-1)}$, matching bounds from [Andoni-Razenshteyn, 2015] and [Dubiner, IEEE-TIT'10] and matching the asymptotic complexities of [Andoni-Razenshteyn, STOC'15] and [Andoni-Indyk-Laarhoven-Razenshteyn-Schmidt, NIPS'15]. A subpolynomial query time complexity $n^{o(1)}$ can be achieved at the cost of a space complexity of the order $n^{1/(4\epsilon^2)}$, matching the bound $n^{\Omega(1/\epsilon^2)}$ of [Andoni-Indyk-Patrascu, FOCS'06] and [Panigrahy-Talwar-Wieder, FOCS'10] and improving upon results of [Indyk-Motwani, STOC'98] and [Kushilevitz-Ostrovsky-Rabani, STOC'98]. #R##N#For large $c$, minimizing the update complexity results in a query complexity of $n^{2/c^2+O(1/c^4)}$, improving upon the related exponent for large $c$ of [Kapralov, PODS'15] by a factor $2$, and matching the bound $n^{\Omega(1/c^2)}$ of [Panigrahy-Talwar-Wieder, FOCS'08]. Balancing the costs leads to optimal complexities $n^{1/(2c^2-1)}$, while a minimum query time complexity can be achieved with update complexity $n^{2/c^2+O(1/c^4)}$, improving upon the previous best exponents of Kapralov by a factor $2$.

Includes members:
  Member "locality sensitive hashing without false negatives":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "similarity problems in high dimensions")
  Member "bucketing coding and information theory for the statistical high dimensional nearest neighbor problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "algorithms for similarity search and pseudorandomness")
  Member "lower bounds on near neighbor search via metric expansion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tight static lower bounds for non adaptive data structures")
  Member "optimal lower bounds for locality sensitive hashing except when q is tiny":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on deep hashing methods")
  Member "entropy based nearest neighbor search in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "algorithms for similarity search and pseudorandomness")

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)