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: "searching in one billion vectors re rank with source coding"
  Abstract: Recent indexing techniques inspired by source coding have been shown successful to index billions of high-dimensional vectors in memory. In this paper, we propose an approach that re-ranks the neighbor hypotheses obtained by these compressed-domain indexing methods. In contrast to the usual post-verification scheme, which performs exact distance calculation on the short-list of hypotheses, the estimated distances are refined based on short quantization codes, to avoid reading the full vectors from disk. We have released a new public dataset of one billion 128-dimensional vectors and proposed an experimental setup to evaluate high dimensional indexing algorithms on a realistic scale. Experiments show that our method accurately and efficiently re-ranks the neighbor hypotheses using little memory compared to the full vectors representation.

Connected through hyperedge HE-1 (induced by source paper "learning to compress and search visual data in large scale systems"):
  with members:
    - "rendition reclaiming what a black box takes away"
    - "polysemous codes"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "billion scale similarity search with gpus"
    - "hashnet deep learning to hash by continuation"
    - "memnet a persistent memory network for image restoration"
    - "full resolution image compression with recurrent neural networks"
    - "end to end optimized image compression"

Connected through hyperedge HE-2 (induced by source paper "unsupervised neural quantization for compressed domain similarity search"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "quasi hyperbolic momentum and adam for deep learning"
    - "subic a supervised structured binary code for image search"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "billion scale similarity search with gpus"
    - "in defense of product quantization"

Connected through hyperedge HE-3 (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-4 (induced by source paper "quicker adc unlocking the hidden potential of product quantization with simd"):
  with members:
    - "derived codebooks for high accuracy nearest neighbor search"
    - "link and code fast indexing with graphs and compact regression codes"
    - "polysemous codes"
    - "faster population counts using avx2 instructions"
    - "revisiting the inverted indices for billion scale approximate nearest neighbors"
    - "subic a supervised structured binary code for image search"
    - "billion scale similarity search with gpus"
    - "cuckoo hash tables high performance hash tables for networking applications"

Connected through hyperedge HE-5 (induced by source paper "aird adversarial learning framework for image repurposing detection"):
  with members:
    - "adversarial complementary learning for weakly supervised object localization"
    - "deep multimodal image repurposing detection"
    - "deep matching and validation network an end to end solution to constrained image splicing localization and detection"
    - "adversarial feature augmentation for unsupervised domain adaptation"
    - "faceposenet making a case for landmark free face alignment"
    - "multimedia semantic integrity assessment using joint embedding of images and text"
    - "csi a hybrid deep model for fake news detection"
    - "do we really need to collect millions of faces for effective face recognition"

Connected through hyperedge HE-6 (induced by source paper "pyramid a general framework for distributed similarity search"):
  with members:
    - "norm ranging lsh for maximum inner product search"
    - "link and code fast indexing with graphs and compact regression codes"
    - "revisiting the inverted indices for billion scale approximate nearest neighbors"
    - "low shot learning with large scale diffusion"
    - "billion scale similarity search with gpus"
    - "fast approximate nearest neighbor search with navigating spreading out graphs"
    - "a survey on learning to hash"
    - "clustering is efficient for approximate maximum inner product search"

Connected through hyperedge HE-7 (induced by source paper "vector and line quantization for billion scale similarity search on gpus"):
  with members:
    - "revisiting the inverted indices for billion scale approximate nearest neighbors"
    - "billion scale similarity search with gpus"
    - "efficient large scale approximate nearest neighbor search on the gpu"
    - "deep residual learning for image recognition"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "improving bilayer product quantization for billion scale approximate nearest neighbors in high dimensions"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-8 (induced by source paper "b bit sketch trie scalable similarity search on integer sketches"):
  with members:
    - "pigeonring a principle for faster thresholded similarity search"
    - "linearized gmm kernels and normalized random fourier features"
    - "fast exact search in hamming space with multi index hashing"
    - "from theory to practice plug and play with succinct data structures"
    - "b bit minwise hashing"

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.IR(Information Retrieval)