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: "bilinear random projections for locality sensitive binary codes"
  Abstract: Locality-sensitive hashing (LSH) is a popular data-independent indexing method for approximate similarity search, where random projections followed by quantization hash the points from the database so as to ensure that the probability of collision is much higher for objects that are close to each other than for those that are far apart. Most of high-dimensional visual descriptors for images exhibit a natural matrix structure. When visual descriptors are represented by high-dimensional feature vectors and long binary codes are assigned, a random projection matrix requires expensive complexities in both space and time. In this paper we analyze a bilinear random projection method where feature matrices are transformed to binary codes by two smaller random projection matrices. We base our theoretical analysis on extending Raginsky and Lazebnik's result where random Fourier features are composed with random binary quantizers to form locality sensitive binary codes. To this end, we answer the following two questions: (1) whether a bilinear random projection also yields similarity-preserving binary codes; (2) whether a bilinear random projection yields performance gain or loss, compared to a large linear projection. Regarding the first question, we present upper and lower bounds on the expected Hamming distance between binary codes produced by bilinear random projections. In regards to the second question, we analyze the upper and lower bounds on covariance between two bits of binary codes, showing that the correlation between two bits is small. Numerical experiments on MNIST and Flickr45K datasets confirm the validity of our method.

Connected through hyperedge HE-1 (induced by source paper "simultaneous feature aggregating and hashing for compact binary code learning"):
  with members:
    - "on device scalable image based localization via prioritized cascade search and fast one many ransac"
    - "deep supervised discrete hashing"
    - "simultaneous feature aggregating and hashing for large scale image search"
    - "embedding based on function approximation for large scale image search"
    - "a survey on learning to hash"
    - "learning to hash with binary deep neural network"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep semantic ranking based hashing for multi label image retrieval"

Connected through hyperedge HE-2 (induced by source paper "bilinear supervised hashing based on 2d image features"):
  with members:
    - "ssdh semi supervised deep hashing for large scale image retrieval"
    - "deep hashing a joint approach for image signature learning"
    - "a survey on learning to hash"
    - "efficient training of very deep neural networks for supervised hashing"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"

Connected through hyperedge HE-3 (induced by source paper "simultaneous feature aggregating and hashing for large scale image search"):
  with members:
    - "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"
    - "learning to hash for indexing big data a survey"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"

Connected through hyperedge HE-4 (induced by source paper "cvpaper challenge in 2015 a review of cvpr2015 and deepsurvey"):
  with members:
    - "semi supervised learning with explicit relationship regularization"
    - "understanding image virality"
    - "watch and learn semi supervised learning of object detectors from videos"
    - "deep carving discovering visual attributes by carving deep neural nets"
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
    - "learning multiple visual tasks while discovering their structure"
    - "facenet a unified embedding for face recognition and clustering"
    - "background subtraction via generalized fused lasso foreground modeling"

Connected through hyperedge HE-5 (induced by source paper "fast binary embedding via circulant downsampled matrix a data independent approach"):
  with members:
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "compressive signal processing with circulant sensing matrices"
    - "achievable angles between two compressed sparse vectors under norm distance constraints imposed by the restricted isometry property a plane geometry approach"

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)