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: "optimized cartesian k means"
  Abstract: Product quantization-based approaches are effective to encode high-dimensional data points for approximate nearest neighbor search. The space is decomposed into a Cartesian product of low-dimensional subspaces, each of which generates a sub codebook. Data points are encoded as compact binary codes using these sub codebooks, and the distance between two data points can be approximated efficiently from their codes by the precomputed lookup tables. Traditionally, to encode a subvector of a data point in a subspace, only one sub codeword in the corresponding sub codebook is selected, which may impose strict restrictions on the search accuracy. In this paper, we propose a novel approach, named Optimized Cartesian $K$-Means (OCKM), to better encode the data points for more accurate approximate nearest neighbor search. In OCKM, multiple sub codewords are used to encode the subvector of a data point in a subspace. Each sub codeword stems from different sub codebooks in each subspace, which are optimally generated with regards to the minimization of the distortion errors. The high-dimensional data point is then encoded as the concatenation of the indices of multiple sub codewords from all the subspaces. This can provide more flexibility and lower distortion errors than traditional methods. Experimental results on the standard real-life datasets demonstrate the superiority over state-of-the-art approaches for approximate nearest neighbor search.

Connected through hyperedge HE-1 (induced by source paper "dhog deep hierarchical object grouping"):
  with members:
    - "on mutual information maximization for representation learning"
    - "deep comprehensive correlation mining for image clustering"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "fast autoaugment"
    - "learning representations by maximizing mutual information across views"
    - "cinic 10 is not imagenet or cifar 10"
    - "deep k means jointly clustering with k means and learning representations"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised clustering by exploiting unique class count"):
  with members:
    - "learned cardinalities estimating correlated joins with deep learning"
    - "predict and constrain modeling cardinality in deep structured prediction"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "wide residual networks"
    - "joint unsupervised learning of deep representations and image clusters"
    - "u net convolutional networks for biomedical image segmentation"
    - "unsupervised deep embedding for clustering analysis"

Connected through hyperedge HE-3 (induced by source paper "adversarial attack on deep product quantization network for image retrieval"):
  with members:
    - "end to end supervised product quantization for image search and retrieval"
    - "unsupervised adversarial attacks on deep feature based retrieval with gan"
    - "deep triplet quantization"
    - "advertorch v0 1 an adversarial robustness toolbox based on pytorch"
    - "adversarial examples for semantic segmentation and object detection"
    - "universal adversarial perturbations"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-4 (induced by source paper "deep discriminative clustering analysis"):
  with members:
    - "variational wasserstein clustering"
    - "deep clustering for unsupervised learning of visual features"
    - "clustergan latent space clustering in generative adversarial networks"
    - "deep continuous clustering"
    - "relational inductive biases deep learning and graph networks"
    - "learning markov clustering networks for scene text detection"
    - "hierarchical clustering with structural constraints"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"

Connected through hyperedge HE-5 (induced by source paper "deep triplet quantization"):
  with members:
    - "subic a supervised structured binary code for image search"
    - "hashnet deep learning to hash by continuation"
    - "composite correlation quantization for efficient multimodal retrieval"
    - "a survey on learning to hash"
    - "learning to hash with binary deep neural network"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "improving deep image clustering with spatial transformer layers"):
  with members:
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "joint unsupervised learning of deep representations and image clusters"
    - "spatial transformer networks"
    - "unsupervised deep embedding for clustering analysis"
    - "very deep convolutional networks for large scale image recognition"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-7 (induced by source paper "a weakly supervised learning based clustering framework"):
  with members:
    - "learned cardinalities estimating correlated joins with deep learning"
    - "a compact network learning model for distribution regression"
    - "predicting time varying distributions with limited training data"
    - "deep sets"
    - "predict and constrain modeling cardinality in deep structured prediction"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "deepsetnet predicting sets with deep neural networks"
    - "group equivariant convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "supervised quantization for similarity search"):
  with members:
    - "hashing on nonlinear manifolds"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "hashing with binary autoencoders"
    - "hashing for similarity search a survey"
    - "revisiting kernelized locality sensitive hashing for improved large scale image retrieval"
    - "inductive hashing on manifolds"
    - "a general two step approach to learning based hashing"
    - "fast neighborhood graph search using cartesian concatenation"

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.CV(Computer Vision and Pattern Recognition)