human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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 $2$.

Connected through hyperedge HE-1 (induced by source paper "algorithms for similarity search and pseudorandomness"):
  with members:
    - "hardness of approximate nearest neighbor search"
    - "fast locality sensitive hashing for approximate near neighbor search"
    - "fast similarity sketching"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "optimal densification for fast and accurate minwise hashing"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "scalable and robust set similarity join"
    - "fast cross polytope locality sensitive hashing"

Connected through hyperedge HE-2 (induced by source paper "subsets and supermajorities unifying hashing based set similarity search"):
  with members:
    - "norm ranging lsh for maximum inner product search"
    - "an equivalence class for orthogonal vectors"
    - "hardness of approximate nearest neighbor search"
    - "fast similarity sketching"
    - "distributed pcp theorems for hardness of approximation in p"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "on the complexity of inner product similarity join"
    - "polynomial representations of threshold functions and algorithmic applications"

Connected through hyperedge HE-3 (induced by source paper "improved quantum information set decoding"):
  with members:
    - "quantum information set decoding algorithms"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"

Connected through hyperedge HE-4 (induced by source paper "parameter free locality sensitive hashing for spherical range reporting"):
  with members:
    - "proximity in the age of distraction robust approximate nearest neighbor search"
    - "probabilistic polynomials and hamming nearest neighbors"

Connected through hyperedge HE-5 (induced by source paper "lower bounds on time space trade offs for approximate near neighbors"):
  with members:
    - "randomized approximate nearest neighbor search with limited adaptivity"
    - "simple average case lower bounds for approximate near neighbor from isoperimetric inequalities"
    - "exploiting the structure stochastic gradient methods using raw clusters"
    - "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time"
    - "scalability and total recall with fast coveringlsh"
    - "learning to hash for indexing big data a survey"
    - "a faster subquadratic algorithm for finding outlier correlations"
    - "tight lower bounds for data dependent locality sensitive hashing"

Connected through hyperedge HE-6 (induced by source paper "set similarity search beyond minhash"):
  with members:
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "on the complexity of inner product similarity join"
    - "tight lower bounds for data dependent locality sensitive hashing"
    - "practical and optimal lsh for angular distance"
    - "optimal data dependent hashing for approximate near neighbors"
    - "beyond locality sensitive hashing"
    - "bottom k and priority sampling set similarity and subset sums with minimal independence"

Connected through hyperedge HE-7 (induced by source paper "sieving for closest lattice vectors with preprocessing"):
  with members:
    - "solving the closest vector problem in 2 n time the discrete gaussian strikes again"
    - "solving the shortest vector problem in 2 n time via discrete gaussian sampling"
    - "hashing for similarity search a survey"
    - "on the closest vector problem with a distance guarantee"

Connected through hyperedge HE-8 (induced by source paper "coveringlsh locality sensitive hashing without false negatives"):
  with members:
    - "optimal las vegas locality sensitive data structures"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "scalability and total recall with fast coveringlsh"
    - "optimal data dependent hashing for approximate near neighbors"
    - "probabilistic polynomials and hamming nearest neighbors"
    - "beyond locality sensitive hashing"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"
    - "entropy based nearest neighbor search in high dimensions"

Question: Please tell me which class the center node 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)