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: "a framework for similarity search with space time tradeoffs using locality sensitive filtering"
  Abstract: We present a framework for similarity search based on Locality-Sensitive Filtering (LSF), generalizing the Indyk-Motwani (STOC 1998) Locality-Sensitive Hashing (LSH) framework to support space-time tradeoffs. Given a family of filters, defined as a distribution over pairs of subsets of space with certain locality-sensitivity properties, we can solve the approximate near neighbor problem in $d$-dimensional space for an $n$-point data set with query time $dn^{\rho_q+o(1)}$, update time $dn^{\rho_u+o(1)}$, and space usage $dn + n^{1 + \rho_u + o(1)}$. The space-time tradeoff is tied to the tradeoff between query time and update time, controlled by the exponents $\rho_q, \rho_u$ that are determined by the filter family. Locality-sensitive filtering was introduced by Becker et al. (SODA 2016) together with a framework yielding a single, balanced, tradeoff between query time and space, further relying on the assumption of an efficient oracle for the filter evaluation algorithm. We extend the LSF framework to support space-time tradeoffs and through a combination of existing techniques we remove the oracle assumption. #R##N#Building on a filter family for the unit sphere by Laarhoven (arXiv 2015) we use a kernel embedding technique by Rahimi & Recht (NIPS 2007) to show a solution to the $(r,cr)$-near neighbor problem in $\ell_s^d$-space for $0 < s \leq 2$ with query and update exponents $\rho_q=\frac{c^s(1+\lambda)^2}{(c^s+\lambda)^2}$ and $\rho_u=\frac{c^s(1-\lambda)^2}{(c^s+\lambda)^2}$ where $\lambda\in[-1,1]$ is a tradeoff parameter. This result improves upon the space-time tradeoff of Kapralov (PODS 2015) and is shown to be optimal in the case of a balanced tradeoff. Finally, we show a lower bound for the space-time tradeoff on the unit sphere that matches Laarhoven's and our own upper bound in the case of random data.

Connected through hyperedge HE-1 (induced by source paper "lsf join locality sensitive filtering for distributed all pairs set similarity under skew"):
  with members:
    - "hardness of bichromatic closest pair with jaccard similarity"
    - "set similarity search for skewed data"
    - "adaptive mapreduce similarity joins"
    - "when hashes met wedges a distributed algorithm for finding high similarity vectors"
    - "scalable and robust set similarity join"
    - "on the complexity of inner product similarity join"
    - "lsh ensemble internet scale domain search"
    - "massively parallel similarity join edge isoperimetry and distance correlations on the hypercube"

Connected through hyperedge HE-2 (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-3 (induced by source paper "similarity problems in high dimensions"):
  with members:
    - "approximate furthest neighbor with application to annulus query"
    - "distributed pcp theorems for hardness of approximation in p"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "set similarity search beyond minhash"
    - "distance sensitive bloom filters without false negatives"
    - "fast and powerful hashing using tabulation"

Connected through hyperedge HE-4 (induced by source paper "on the hardness of approximate and exact bichromatic maximum inner product"):
  with members:
    - "hardness of approximate nearest neighbor search"
    - "conditional lower bounds for all pairs max flow"
    - "conditional lower bounds for space time tradeoffs"
    - "distributed pcp theorems for hardness of approximation in p"
    - "on the parameterized complexity of approximating dominating set"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "on the complexity of closest pair via polar pair of point sets"
    - "on the complexity of inner product similarity join"

Connected through hyperedge HE-5 (induced by source paper "set similarity search for skewed data"):
  with members:
    - "distributed pcp theorems for hardness of approximation in p"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "scalable and robust set similarity join"
    - "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"
    - "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time"
    - "set similarity search beyond minhash"

Connected through hyperedge HE-6 (induced by source paper "improved quantum information set decoding"):
  with members:
    - "quantum information set decoding algorithms"
    - "tradeoffs for nearest neighbors on the sphere"

Connected through hyperedge HE-7 (induced by source paper "distributed pcp theorems for hardness of approximation in p"):
  with members:
    - "fully dynamic maximal matching in constant update time"
    - "new deterministic approximation algorithms for fully dynamic matching"
    - "the curse of medium dimension for geometric problems in almost every norm"
    - "on the hardness of partially dynamic graph problems and connections to diameter"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "on the complexity of inner product similarity join"
    - "design of dynamic algorithms via primal dual method"
    - "on problems as hard as cnf sat"

Connected through hyperedge HE-8 (induced by source paper "distance sensitive hashing"):
  with members:
    - "approximate furthest neighbor with application to annulus query"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "sub linear privacy preserving near neighbor search with untrusted server on large scale datasets"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "on the complexity of inner product similarity join"
    - "set similarity search beyond minhash"
    - "tight lower bounds for data dependent locality sensitive hashing"

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)