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: "deep reinforcement learning for image hashing"
  Abstract: Deep hashing methods have received much attention recently, which achieve promising results by taking advantage of the strong representation power of deep networks. However, most existing deep hashing methods learn a whole set of hashing functions independently and directly, while ignore the correlation between different hashing functions that can promote the retrieval accuracy greatly. Inspired by the sequential decision ability of deep reinforcement learning, in this paper, we propose a new Deep Reinforcement Learning approach for Image Hashing (DRLIH). Our proposed DRLIH models the hashing learning problem as a Markov Decision Process (MDP), which learns each hashing function by correcting the errors imposed by previous ones and promotes retrieval accuracy. To the best of our knowledge, this is the first work that tries to address hashing problem from deep reinforcement learning perspective. The main contributions of our proposed DRLIH approach can be summarized as follows: (1) We propose a deep reinforcement learning hashing network. In our proposed DRLIH approach, we utilize recurrent neural network (RNN) as agents to model the hashing functions, which take actions of projecting images into binary codes sequentially, so that current hashing function learning can take previous hashing functions' error into account. (2) We propose a sequential learning strategy based on proposed DRLIH. We define the state as a tuple of internal features of RNN's hidden layers and image features, which can well reflect history decisions made by the agents. We also propose an action group method to enhance the correlation of the hash functions in the same group. Experiments on three widely-used datasets demonstrate the effectiveness of our proposed DRLIH approach.

Connected through hyperedge HE-1 (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"
    - "ssdh semi supervised deep hashing for large scale image retrieval"
    - "multi attention multi class constraint for fine grained image recognition"
    - "deep semantic hashing with generative adversarial networks"
    - "learning to navigate for fine grained classification"

Connected through hyperedge HE-2 (induced by source paper "semantic cluster unary loss for efficient deep hashing"):
  with members:
    - "ssdh semi supervised deep hashing for large scale image retrieval"
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "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-3 (induced by source paper "deep policy hashing network with listwise supervision"):
  with members:
    - "fast supervised discrete hashing"
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "blockdrop dynamic inference paths in residual networks"
    - "hashnet deep learning to hash by continuation"
    - "fast training of triplet based deep binary embedding networks"
    - "a survey on learning to hash"
    - "asynchronous methods for deep reinforcement learning"
    - "simultaneous feature learning and hash coding with deep neural networks"

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)