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: "hashnet deep learning to hash by continuation"
  Abstract: Learning to hash has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval, due to its computation efficiency and retrieval quality. Deep learning to hash, which improves retrieval quality by end-to-end representation learning and hash encoding, has received increasing attention recently. Subject to the ill-posed gradient difficulty in the optimization with sign activations, existing deep learning to hash methods need to first learn continuous representations and then generate binary hash codes in a separated binarization step, which suffer from substantial loss of retrieval quality. This work presents HashNet, a novel deep architecture for deep learning to hash by continuation method with convergence guarantees, which learns exactly binary hash codes from imbalanced similarity data. The key idea is to attack the ill-posed gradient problem in optimizing deep networks with non-smooth binary activations by continuation method, in which we begin from learning an easier network with smoothed activation function and let it evolve during the training, until it eventually goes back to being the original, difficult to optimize, deep network with the sign activation function. Comprehensive empirical evidence shows that HashNet can generate exactly binary hash codes and yield state-of-the-art multimedia retrieval performance on standard benchmarks.

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 "deep image to video adaptation and fusion networks for action recognition"):
  with members:
    - "hierarchically learned view invariant representations for cross view action recognition"
    - "combined static and motion features for deep networks based activity recognition in videos"
    - "transferable feature representation for visible to infrared cross dataset human action recognition"
    - "global temporal representation based cnns for infrared action recognition"
    - "deep cross media knowledge transfer"
    - "exploiting images for video recognition with hierarchical generative adversarial networks"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "semantic autoencoder for zero shot learning"

Connected through hyperedge HE-3 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach"):
  with members:
    - "neo a learned query optimizer"
    - "selectivity estimation with deep likelihood models"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "an empirical analysis of deep learning for cardinality estimation"
    - "multi attribute selectivity estimation using deep learning"
    - "an end to end learning based cost estimator"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning state representations for query optimization with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "learning to hash with graph neural networks for recommender systems"):
  with members:
    - "is a single vector enough exploring node polysemy for network embedding"
    - "deep representation learning for social network analysis"
    - "graph neural networks for social recommendation"
    - "neural graph collaborative filtering"
    - "deep learning based recommender system a survey and new perspectives"
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-5 (induced by source paper "deep heterogeneous hashing for face video retrieval"):
  with members:
    - "the do s and don ts for cnn based face verification"
    - "is second order information helpful for large scale visual recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "fast training of triplet based deep binary embedding networks"
    - "deep supervised hashing with triplet labels"
    - "deep cross modal hashing"
    - "a riemannian network for spd matrix learning"
    - "umdfaces an annotated face dataset for training deep networks"

Connected through hyperedge HE-6 (induced by source paper "end to end supervised product quantization for image search and retrieval"):
  with members:
    - "learning hash codes via hamming distance targets"
    - "deep supervised discrete hashing"
    - "billion scale similarity search with gpus"
    - "deep image retrieval learning global representations for image search"
    - "deep supervised hashing with triplet labels"
    - "identity mappings in deep residual networks"
    - "learning to hash with binary deep neural network"
    - "efficient training of very deep neural networks for supervised hashing"

Connected through hyperedge HE-7 (induced by source paper "auto encoding twin bottleneck hashing"):
  with members:
    - "distillhash unsupervised deep hashing by distilling data pairs"
    - "embarrassingly simple binary representation learning"
    - "zero shot sketch image hashing"
    - "bingan learning compact binary descriptors with a regularized gan"
    - "stochastic generative hashing"
    - "semi supervised classification with graph convolutional networks"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "hashing with binary autoencoders"

Connected through hyperedge HE-8 (induced by source paper "training progressively binarizing deep networks using fpgas"):
  with members:
    - "accelerating deterministic and stochastic binarized neural networks on fpgas using opencl"
    - "self binarizing networks"
    - "towards binary valued gates for robust lstm training"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "bnn improved binary network training"
    - "towards accurate binary convolutional neural network"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "adam a method for stochastic optimization"

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.LG(Machine Learning)