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: "how should we evaluate supervised hashing"
  Abstract: Hashing produces compact representations for documents, to perform tasks like classification or retrieval based on these short codes. When hashing is supervised, the codes are trained using labels on the training data. This paper first shows that the evaluation protocols used in the literature for supervised hashing are not satisfactory: we show that a trivial solution that encodes the output of a classifier significantly outperforms existing supervised or semi-supervised methods, while using much shorter codes. We then propose two alternative protocols for supervised hashing: one based on retrieval on a disjoint set of classes, and another based on transfer learning to new classes. We provide two baseline methods for image-related tasks to assess the performance of (semi-)supervised hashing: without coding and with unsupervised codes. These baselines give a lower- and upper-bound on the performance of a supervised hashing scheme.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "deep supervised hashing leveraging quadratic spherical mutual information for content based image retrieval"):
  with members:
    - "triplet based deep hashing network for cross modal retrieval"
    - "mihash online hashing with mutual information"
    - "deep supervised discrete hashing"
    - "asymmetric deep supervised hashing"
    - "hashnet deep learning to hash by continuation"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep supervised hashing with triplet labels"
    - "densely connected convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "unsupervised neural quantization for compressed domain similarity search"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "quasi hyperbolic momentum and adam for deep learning"
    - "subic a supervised structured binary code for image search"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "billion scale similarity search with gpus"
    - "in defense of product quantization"

Connected through hyperedge HE-4 (induced by source paper "unsupervised rank preserving hashing for large scale image retrieval"):
  with members:
    - "link and code fast indexing with graphs and compact regression codes"
    - "polysemous codes"
    - "deep binary reconstruction for cross modal hashing"
    - "patternnet visual pattern mining with deep neural network"
    - "a survey on learning to hash"
    - "a revisit of hashing algorithms for approximate nearest neighbor search"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-5 (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"
    - "ssdh semi supervised deep hashing for large scale image retrieval"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "ssdh semi supervised deep hashing for large scale image retrieval"):
  with members:
    - "a survey on learning to hash"
    - "hashing on nonlinear manifolds"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "return of the devil in the details delving deep into convolutional nets"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-7 (induced by source paper "deep spherical quantization for image search"):
  with members:
    - "deep triplet quantization"
    - "cosface large margin cosine loss for deep face recognition"
    - "triplet center loss for multi view 3d object retrieval"
    - "subic a supervised structured binary code for image search"
    - "deep supervised discrete hashing"
    - "l2 constrained softmax loss for discriminative face verification"
    - "hashnet deep learning to hash by continuation"
    - "a survey on learning to hash"

Connected through hyperedge HE-8 (induced by source paper "supervised online hashing via similarity distribution learning"):
  with members:
    - "towards optimal discrete online hashing with balanced similarity"
    - "hadamard matrix guided online hashing"
    - "mihash online hashing with mutual information"
    - "ordinal constrained binary code learning for nearest neighbor search"
    - "learning to hash for indexing big data a survey"
    - "very deep convolutional networks for large scale image recognition"
    - "simple and deterministic matrix sketching"

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)