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: "fast k nearest neighbour search via dynamic continuous indexing"
  Abstract: Existing methods for retrieving k-nearest neighbours suffer from the curse of dimensionality. We argue this is caused in part by inherent deficiencies of space partitioning, which is the underlying strategy used by most existing methods. We devise a new strategy that avoids partitioning the vector space and present a novel randomized algorithm that runs in time linear in dimensionality of the space and sub-linear in the intrinsic dimensionality and the size of the dataset and takes space constant in dimensionality of the space and linear in the size of the dataset. The proposed algorithm allows fine-grained control over accuracy and speed on a per-query basis, automatically adapts to variations in data density, supports dynamic updates to the dataset and is easy-to-implement. We show appealing theoretical properties and demonstrate empirically that the proposed algorithm outperforms locality-sensitivity hashing (LSH) in terms of approximation quality, speed and space efficiency.

Connected through hyperedge HE-1 (induced by source paper "implicit maximum likelihood estimation"):
  with members:
    - "fast k nearest neighbour search via prioritized dci"
    - "the numerics of gans"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "do gans actually learn the distribution an empirical study"
    - "flow gan combining maximum likelihood and adversarial learning in generative models"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "energy based generative adversarial network"
    - "generating images with perceptual similarity metrics based on deep networks"

Connected through hyperedge HE-2 (induced by source paper "efficient learning of neighbor representations for boundary trees and forests"):
  with members:
    - "learning deep nearest neighbor representations using differentiable boundary trees"
    - "in datacenter performance analysis of a tensor processing unit"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "the mythos of model interpretability"
    - "the boundary forest algorithm for online supervised and unsupervised learning"
    - "optimal data dependent hashing for approximate near neighbors"
    - "adam a method for stochastic optimization"
    - "deep metric learning using triplet network"

Connected through hyperedge HE-3 (induced by source paper "super resolution via conditional implicit maximum likelihood estimation"):
  with members:
    - "implicit maximum likelihood estimation"
    - "fast k nearest neighbour search via prioritized dci"
    - "do gans actually learn the distribution an empirical study"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "super resolution with deep convolutional sufficient statistics"
    - "image super resolution using deep convolutional networks"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-4 (induced by source paper "fast k nearest neighbour search via prioritized dci"):
  with members:
    - "optimal data dependent hashing for approximate near neighbors"
    - "low quality dimension reduction and high dimensional approximate nearest neighbor"

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)