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: "ssdh semi supervised deep hashing for large scale image retrieval"
  Abstract: Hashing methods have been widely used for efficient similarity retrieval on large scale image database. Traditional hashing methods learn hash functions to generate binary codes from hand-crafted features, which achieve limited accuracy since the hand-crafted features cannot optimally represent the image content and preserve the semantic similarity. Recently, several deep hashing methods have shown better performance because the deep architectures generate more discriminative feature representations. However, these deep hashing methods are mainly designed for supervised scenarios, which only exploit the semantic similarity information, but ignore the underlying data structures. In this paper, we propose the semi-supervised deep hashing approach, to perform more effective hash function learning by simultaneously preserving semantic similarity and underlying data structures. The main contributions are as follows: 1) We propose a  semi-supervised loss  to jointly minimize the empirical error on labeled data, as well as the embedding error on both labeled and unlabeled data, which can preserve the semantic similarity and capture the meaningful neighbors on the underlying data structures for effective hashing. 2) A  semi-supervised deep hashing network  is designed to extensively exploit both labeled and unlabeled data, in which we propose an  online graph construction  method to benefit from the evolving deep features during training to better capture semantic neighbors. To the best of our knowledge, the proposed deep network is the  first  deep hashing method that can perform hash code learning and feature learning simultaneously in a semi-supervised fashion. Experimental results on five widely-used data sets show that our proposed approach outperforms the state-of-the-art hashing methods.

Connected through hyperedge HE-1 (induced by source paper "generalized product quantization network for semi supervised image retrieval"):
  with members:
    - "end to end supervised product quantization for image search and retrieval"
    - "deep triplet quantization"
    - "a closer look at few shot classification"
    - "open set domain adaptation by backpropagation"
    - "subic a supervised structured binary code for image search"
    - "prototypical networks for few shot learning"
    - "understanding deep learning requires rethinking generalization"
    - "scalable image retrieval by sparse product quantization"

Connected through hyperedge HE-2 (induced by source paper "simultaneous region localization and hash coding for fine grained image retrieval"):
  with members:
    - "fast supervised discrete hashing"
    - "deep policy hashing network with listwise supervision"
    - "deep triplet quantization"
    - "feature pyramid hashing"
    - "multi attention multi class constraint for fine grained image recognition"
    - "deep semantic hashing with generative adversarial networks"
    - "learning to navigate for fine grained classification"
    - "deep saliency hashing"

Connected through hyperedge HE-3 (induced by source paper "semantic cluster unary loss for efficient deep hashing"):
  with members:
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "deep reinforcement learning for image hashing"
    - "mihash online hashing with mutual information"
    - "deep supervised discrete hashing"
    - "sphereface deep hypersphere embedding for face recognition"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "no fuss distance metric learning using proxies"
    - "smooth neighbors on teacher graphs for semi supervised learning"

Connected through hyperedge HE-4 (induced by source paper "ssah semi supervised adversarial deep hashing with self paced hard sample generation"):
  with members:
    - "deep triplet quantization"
    - "towards optimal discrete online hashing with balanced similarity"
    - "deep semantic hashing with generative adversarial networks"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "deep saliency hashing"
    - "ganimation anatomically aware facial animation from a single image"
    - "random erasing data augmentation"
    - "a fast rcnn hard positive generation via adversary for object detection"

Connected through hyperedge HE-5 (induced by source paper "a large scale deep architecture for personalized grocery basket recommendations"):
  with members:
    - "complementary similarity learning using quadruplet network"
    - "modeling complementary products and customer preferences with context knowledge for online recommendation"
    - "complete the look scene based complementary product recommendation"
    - "inferring complementary products from baskets and browsing sessions"
    - "billion scale similarity search with gpus"
    - "item2vec neural item embedding for collaborative filtering"
    - "tensorflow a system for large scale machine learning"
    - "learning visual clothing style with heterogeneous dyadic co occurrences"

Connected through hyperedge HE-6 (induced by source paper "pairwise teacher student network for semi supervised hashing"):
  with members:
    - "deep semantic hashing with generative adversarial networks"
    - "hashing with mutual information"
    - "deep supervised discrete hashing"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "hashnet deep learning to hash by continuation"
    - "smooth neighbors on teacher graphs for semi supervised learning"
    - "temporal ensembling for semi supervised learning"
    - "improved techniques for training gans"

Connected through hyperedge HE-7 (induced by source paper "bilinear supervised hashing based on 2d image features"):
  with members:
    - "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"
    - "bilinear random projections for locality sensitive binary codes"
    - "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-8 (induced by source paper "deep reinforcement learning for image hashing"):
  with members:
    - "deep reinforcement learning based image captioning with embedding reward"
    - "hashnet deep learning to hash by continuation"
    - "a survey on learning to hash"
    - "query adaptive image retrieval by deep weighted 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"

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)