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: "simultaneous feature aggregating and hashing for large scale image search"
  Abstract: In most state-of-the-art hashing-based visual search systems, local image descriptors of an image are first aggregated as a single feature vector. This feature vector is then subjected to a hashing function that produces a binary hash code. In previous work, the aggregating and the hashing processes are designed independently. In this paper, we propose a novel framework where feature aggregating and hashing are designed simultaneously and optimized jointly. Specifically, our joint optimization produces aggregated representations that can be better reconstructed by some binary codes. This leads to more discriminative binary hash codes and improved retrieval accuracy. In addition, we also propose a fast version of the recently-proposed Binary Autoencoder to be used in our proposed framework. We perform extensive retrieval experiments on several benchmark datasets with both SIFT and convolutional features. Our results suggest that the proposed framework achieves significant improvements over the state of the art.

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

Connected through hyperedge HE-2 (induced by source paper "on device scalable image based localization via prioritized cascade search and fast one many ransac"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "image based localization using hourglass networks"
    - "selective deep convolutional features for image retrieval"
    - "image based localization using lstms for structured feature correlation"
    - "binary hashing with semidefinite relaxation and augmented lagrangian"
    - "learning to hash with binary deep neural network"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "metric localization using google street view"

Connected through hyperedge HE-3 (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"
    - "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"
    - "embedding based on function approximation for large scale image search"
    - "a survey on learning to hash"

Connected through hyperedge HE-4 (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"
    - "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"
    - "embedding based on function approximation for large scale image search"
    - "learning to hash with binary deep neural network"

Connected through hyperedge HE-5 (induced by source paper "selective deep convolutional features for image retrieval"):
  with members:
    - "deep image retrieval learning global representations for image search"
    - "embedding based on function approximation for large scale 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-6 (induced by source paper "supervised hashing with end to end binary deep neural network"):
  with members:
    - "fast training of triplet based deep binary embedding networks"
    - "embedding based on function approximation for large scale image search"
    - "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"

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)