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: "hypercube lsh for approximate near neighbors"
  Abstract: A celebrated technique for finding near neighbors for the angular distance involves using a set of \textit{random} hyperplanes to partition the space into hash regions [Charikar, STOC 2002]. Experiments later showed that using a set of \textit{orthogonal} hyperplanes, thereby partitioning the space into the Voronoi regions induced by a hypercube, leads to even better results [Terasawa and Tanaka, WADS 2007]. However, no theoretical explanation for this improvement was ever given, and it remained unclear how the resulting hypercube hash method scales in high dimensions. #R##N#In this work, we provide explicit asymptotics for the collision probabilities when using hypercubes to partition the space. For instance, two near-orthogonal vectors are expected to collide with probability $(\frac{1}{\pi})^{d + o(d)}$ in dimension $d$, compared to $(\frac{1}{2})^d$ when using random hyperplanes. Vectors at angle $\frac{\pi}{3}$ collide with probability $(\frac{\sqrt{3}}{\pi})^{d + o(d)}$, compared to $(\frac{2}{3})^d$ for random hyperplanes, and near-parallel vectors collide with similar asymptotic probabilities in both cases. #R##N#For $c$-approximate nearest neighbor searching, this translates to a decrease in the exponent $\rho$ of locality-sensitive hashing (LSH) methods of a factor up to $\log_2(\pi) \approx 1.652$ compared to hyperplane LSH. For $c = 2$, we obtain $\rho \approx 0.302 + o(1)$ for hypercube LSH, improving upon the $\rho \approx 0.377$ for hyperplane LSH. We further describe how to use hypercube LSH in practice, and we consider an example application in the area of lattice algorithms.

Connected through hyperedge HE-1 (induced by source paper "polytopes lattices and spherical codes for the nearest neighbor problem"):
  with members:
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "fast cross polytope locality sensitive hashing"
    - "practical and optimal lsh for angular distance"
    - "bucketing coding and information theory for the statistical high dimensional nearest neighbor problem"

Connected through hyperedge HE-2 (induced by source paper "faster tuple lattice sieving using spherical locality sensitive filters"):
  with members:
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"

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)