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: "high dimensional spherical range reporting by output sensitive multi probing lsh"
  Abstract: We present a data structure for spherical range reporting on a point set $S$, i.e., reporting all points in $S$ that lie within radius $r$ of a given query point $q$ (with a small probability of error). Our solution builds upon the Locality-Sensitive Hashing (LSH) framework of Indyk and Motwani, which represents the asymptotically best solutions to near neighbor problems in high dimensions. While traditional LSH data structures have several parameters whose optimal values depend on the distance distribution from $q$ to the points of $S$, our data structure is parameter-free. Nevertheless, its time and space usage essentially matches those of an LSH data structure whose parameters have been optimally chosen for the data {\em and query} in question. In particular, our data structure provides a smooth trade-off between hard queries (typically addressed by standard LSH parameter settings) and easy queries such as those where the number of points to report is a constant fraction of $S$, or where almost all points in $S$ are far away from the query point. To our knowledge, this is the first output-sensitive data structure for spherical range reporting in high dimensions. If $t$ is the number of points to report, we propose an algorithm that has query time upper bounded by $O(t (n/t)^\rho)$, where $\rho\in (0,1)$ depends on the data distribution and the strength of the LSH family used. We further present a parameter free way of using multi-probing, for LSH families that support it, and show that for many such families this approach allows us to get query time close to $O(n^\rho+t)$, which is the best we can hope to achieve using LSH. This improves on the running time of $O(t n^\rho)$ achieved by traditional LSH-based data structures where parameters are tuned for outputting a single point within distance $r$, and for large $d$ on bounds by Arya et al. (ESA'10).

Connected through hyperedge HE-1 (induced by source paper "hybrid lsh faster near neighbors reporting in high dimensional space"):
  with members:
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "locality sensitive hashing without false negatives"
    - "probabilistic polynomials and hamming nearest neighbors"

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)