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: "embedding based on function approximation for large scale image search"
  Abstract: The objective of this paper is to design an embedding method that maps local features describing an image (e.g. SIFT) to a higher dimensional representation useful for the image retrieval problem. First, motivated by the relationship between the linear approximation of a nonlinear function in high dimensional space and the stateof-the-art feature representation used in image retrieval, i.e., VLAD, we propose a new approach for the approximation. The embedded vectors resulted by the function approximation process are then aggregated to form a single representation for image retrieval. Second, in order to make the proposed embedding method applicable to large scale problem, we further derive its fast version in which the embedded vectors can be efficiently computed, i.e., in the closed-form. We compare the proposed embedding methods with the state of the art in the context of image search under various settings: when the images are represented by medium length vectors, short vectors, or binary vectors. The experimental results show that the proposed embedding methods outperform existing the state of the art on the standard public image retrieval benchmarks.

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"
    - "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"
    - "bilinear random projections for locality sensitive binary codes"

Connected through hyperedge HE-2 (induced by source paper "binary constrained deep hashing network for image retrieval without human intervention"):
  with members:
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "simultaneous feature aggregating and hashing for large scale image search"
    - "selective deep convolutional features for image retrieval"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "smart mining for deep metric learning"
    - "faster r cnn features for instance search"
    - "fast training of triplet based deep binary embedding networks"
    - "a survey on learning to hash"

Connected through hyperedge HE-3 (induced by source paper "from selective deep convolutional features to compact binary representations for image retrieval"):
  with members:
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "simultaneous feature aggregating and hashing for large scale image search"
    - "exploiting depth from single monocular images for object detection and semantic segmentation"
    - "selective deep convolutional features for image retrieval"
    - "fine tuning cnn image retrieval with no human annotation"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "deep image retrieval learning global representations for image search"
    - "learning to hash with binary deep neural network"

Connected through hyperedge HE-4 (induced by source paper "deep feature aggregation and image re ranking with heat diffusion for image retrieval"):
  with members:
    - "unsupervised part based weighting aggregation of deep convolutional features for image retrieval"
    - "selective deep convolutional features for image retrieval"
    - "fast spectral ranking for similarity search"
    - "siamese network of deep fisher vector descriptors for image retrieval"
    - "voronoi based compact image descriptors efficient region of interest retrieval with vlad and deep learning based descriptors"
    - "deep image retrieval learning global representations for image search"
    - "large scale image retrieval with attentive deep local features"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"

Connected through hyperedge HE-5 (induced by source paper "compact hash code learning with binary deep neural network"):
  with members:
    - "selective deep convolutional features for image retrieval"
    - "hashnet deep learning to hash by continuation"
    - "binary hashing with semidefinite relaxation and augmented lagrangian"
    - "a survey on learning to hash"
    - "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-6 (induced by source paper "selective deep convolutional features for image retrieval"):
  with members:
    - "simultaneous feature aggregating and hashing for large scale image search"
    - "deep image retrieval learning global representations for image search"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "learning to hash with binary deep neural network"
    - "yfcc100m the new data in multimedia research"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "supervised hashing with end to end binary deep neural network"):
  with members:
    - "simultaneous feature aggregating and hashing for large scale image search"
    - "fast training of triplet based deep binary embedding networks"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "enhance feature discrimination for unsupervised hashing"):
  with members:
    - "very deep convolutional networks for large scale image recognition"

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)