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: "anti sparse coding for approximate nearest neighbor search"
  Abstract: This paper proposes a binarization scheme for vectors of high dimension based on the recent concept of anti-sparse coding, and shows its excellent performance for approximate nearest neighbor search. Unlike other binarization schemes, this framework allows, up to a scaling factor, the explicit reconstruction from the binary representation of the original vector. The paper also shows that random projections which are used in Locality Sensitive Hashing algorithms, are significantly outperformed by regular frames for both synthetic and real data if the number of bits exceeds the vector dimensionality, i.e., when high precision is required.

Connected through hyperedge HE-1 (induced by source paper "unsupervised rank preserving hashing for large scale image retrieval"):
  with members:
    - "link and code fast indexing with graphs and compact regression codes"
    - "polysemous codes"
    - "deep binary reconstruction for cross modal hashing"
    - "patternnet visual pattern mining with deep neural network"
    - "a survey on learning to hash"
    - "a revisit of hashing algorithms for approximate nearest neighbor search"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "how should we evaluate supervised hashing"

Connected through hyperedge HE-2 (induced by source paper "safe squeezing for antisparse coding"):
  with members:
    - "screening rules for lasso with non convex sparse regularizers"
    - "screening tests for lasso problems"
    - "par aware large scale multi user mimo ofdm downlink"
    - "safe feature elimination in sparse supervised learning"

Connected through hyperedge HE-3 (induced by source paper "a survey on learning to hash"):
  with members:
    - "collaborative quantization for cross modal similarity search"
    - "scalable discrete supervised hash learning with asymmetric matrix factorization"
    - "approximate search with quantized sparse representations"
    - "binary hashing with semidefinite relaxation and augmented lagrangian"
    - "learning to hash with binary deep neural network"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "hashing with binary autoencoders"

Connected through hyperedge HE-4 (induced by source paper "hashing for similarity search a survey"):
  with members:
    - "optimized cartesian k means"
    - "fast supervised hashing with decision trees for high dimensional data"
    - "inner product similarity search using compositional codes"
    - "beyond locality sensitive hashing"
    - "learning hash functions using column generation"
    - "inductive hashing on manifolds"
    - "coding for random projections"
    - "a general two step approach to learning based hashing"

Connected through hyperedge HE-5 (induced by source paper "efficient on the fly category retrieval using convnets and gpus"):
  with members:
    - "return of the devil in the details delving deep into convolutional nets"
    - "caffe convolutional architecture for fast feature embedding"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "visualizing and understanding convolutional networks"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

Connected through hyperedge HE-6 (induced by source paper "a field guide to forward backward splitting with a fasta implementation"):
  with members:
    - "algorithms initializations and convergence for the nonnegative matrix factorization"
    - "fast approximations to structured sparse coding and applications to object classification"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "low rank matrix completion with noisy observations a quantitative comparison"

Connected through hyperedge HE-7 (induced by source paper "living on the edge phase transitions in convex programs with random data"):
  with members:
    - "corrupted sensing novel guarantees for separating structured signals"
    - "the achievable performance of convex demixing"
    - "regularly random duality"
    - "the phase transition of matrix recovery from gaussian measurements matches the minimax mse of matrix denoising"
    - "sharp mse bounds for proximal denoising"
    - "sharp recovery bounds for convex demixing with applications"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "robust polyhedral regularization"):
  with members:
    - "robust sparse analysis regularization"
    - "structured sparsity inducing norms through submodular functions"

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)