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 discrete supervised hashing"
  Abstract: Hashing has been widely used for large-scale search due to its low storage cost and fast query speed. By using supervised information, supervised hashing can significantly outperform unsupervised hashing. Recently, discrete supervised hashing and feature learning based deep hashing are two representative progresses in supervised hashing. On one hand, hashing is essentially a discrete optimization problem. Hence, utilizing supervised information to directly guide discrete (binary) coding procedure can avoid sub-optimal solution and improve the accuracy. On the other hand, feature learning based deep hashing, which integrates deep feature learning and hash-code learning into an end-to-end architecture, can enhance the feedback between feature learning and hash-code learning. The key in discrete supervised hashing is to adopt supervised information to directly guide the discrete coding procedure in hashing. The key in deep hashing is to adopt the supervised information to directly guide the deep feature learning procedure. However, most deep supervised hashing methods cannot use the supervised information to directly guide both discrete (binary) coding procedure and deep feature learning procedure in the same framework. In this paper, we propose a novel deep hashing method, called deep discrete supervised hashing (DDSH). DDSH is the first deep hashing method which can utilize pairwise supervised information to directly guide both discrete coding procedure and deep feature learning procedure and thus enhance the feedback between these two important procedures. Experiments on four real datasets show that DDSH can outperform other state-of-the-art baselines, including both discrete hashing and deep hashing baselines, for image retrieval.

Connected through hyperedge HE-1 (induced by source paper "a survey on deep hashing methods"):
  with members:
    - "end to end supervised product quantization for image search and retrieval"
    - "distillhash unsupervised deep hashing by distilling data pairs"
    - "deep triplet quantization"
    - "hadamard codebook based deep hashing"
    - "central similarity hashing via hadamard matrix"
    - "deep attention guided hashing"
    - "deep spherical quantization for image search"
    - "deep ordinal hashing with spatial attention"

Connected through hyperedge HE-2 (induced by source paper "candidate generation with binary codes for large scale top n recommendation"):
  with members:
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "discrete factorization machines for fast feature based recommendation"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "the case for learned index structures"
    - "neural collaborative filtering"
    - "translation based recommendation"
    - "asymmetric deep supervised hashing"

Connected through hyperedge HE-3 (induced by source paper "asymmetric deep semantic quantization for image retrieval"):
  with members:
    - "triplet based deep hashing network for cross modal retrieval"
    - "deep attention guided hashing"
    - "deep ordinal hashing with spatial attention"
    - "deep priority hashing"
    - "deep supervised discrete hashing"
    - "asymmetric deep supervised hashing"
    - "a survey on learning to hash"
    - "deep cross modal hashing"

Connected through hyperedge HE-4 (induced by source paper "hierarchy neighborhood discriminative hashing for an unified view of single label and multi label image retrieval"):
  with members:
    - "deep supervised discrete hashing"
    - "rethinking feature discrimination and polymerization for large scale recognition"
    - "hashnet deep learning to hash by continuation"
    - "deep supervised hashing with triplet labels"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"

Connected through hyperedge HE-5 (induced by source paper "pdh probabilistic deep hashing based on map estimation of hamming distance"):
  with members:
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "random erasing data augmentation"
    - "a survey on learning to hash"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "neurons merging layer towards progressive redundancy reduction for deep supervised hashing"):
  with members:
    - "zero shot sketch image hashing"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "hashing with binary matrix pursuit"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "deep supervised discrete hashing"
    - "nisp pruning networks using neuron importance score propagation"
    - "foresthash semantic hashing with shallow random forests and tiny convolutional networks"
    - "asymmetric deep supervised hashing"

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.IR(Information Retrieval)