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: "self supervised video hashing with hierarchical binary auto encoder"
  Abstract: Existing video hash functions are built on three isolated stages: frame pooling, relaxed learning, and binarization, which have not adequately explored the temporal order of video frames in a joint binary optimization model, resulting in severe information loss. In this paper, we propose a novel unsupervised video hashing framework dubbed self-supervised video hashing (SSVH), which is able to capture the temporal nature of videos in an end-to-end learning to hash fashion. We specifically address two central problems: 1) how to design an encoder–decoder architecture to generate binary codes for videos and 2) how to equip the binary codes with the ability of accurate video retrieval. We design a hierarchical binary auto-encoder to model the temporal dependencies in videos with multiple granularities, and embed the videos into binary codes with less computations than the stacked architecture. Then, we encourage the binary codes to simultaneously reconstruct the visual content and neighborhood structure of the videos. Experiments on two real-world data sets show that our SSVH method can significantly outperform the state-of-the-art methods and achieve the current best performance on the task of unsupervised video retrieval.

Connected through hyperedge HE-1 (induced by source paper "fine grained instance level sketch based video retrieval"):
  with members:
    - "deep learning for free hand sketch a survey"
    - "deep self supervised representation learning for free hand sketch"
    - "livesketch query perturbations for guided sketch based visual search"
    - "deep zero shot learning for scene sketch"
    - "learning deep sketch abstraction"
    - "multi fiber networks for video recognition"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "spatio temporal channel correlation networks for action classification"

Connected through hyperedge HE-2 (induced by source paper "adaptive exploration for unsupervised person re identification"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "auto reid searching for a part aware convnet for person re identification"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "active multi kernel domain adaptation for hyperspectral image classification"
    - "dissecting person re identification from the viewpoint of viewpoint"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "person search via a mask guided two stream cnn model"

Connected through hyperedge HE-3 (induced by source paper "semi heterogeneous three way joint embedding network for sketch based image retrieval"):
  with members:
    - "person re identification by semantic region representation and topology constraint"
    - "multi attention multi class constraint for fine grained image recognition"
    - "learning deep sketch abstraction"
    - "harmonious attention network for person re identification"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "learning to sketch with shortcut cycle consistency"
    - "asymmetric feature maps with application to sketch based retrieval"
    - "squeeze and excitation networks"

Connected through hyperedge HE-4 (induced by source paper "video segment copy detection using memory constrained hierarchical batch normalized lstm autoencoder"):
  with members:
    - "hashnet deep learning to hash by continuation"
    - "a survey on learning to hash"
    - "recurrent batch normalization"
    - "hashing on nonlinear manifolds"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "analysis of evolutionary behavior in self learning media search engines"):
  with members:
    - "triplet based deep hashing network for cross modal retrieval"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "optimizing query evaluations using reinforcement learning for web search"
    - "sketch based manga retrieval using manga109 dataset"
    - "cross media similarity evaluation for web image retrieval in the wild"
    - "interactive spoken content retrieval by deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "visil fine grained spatio temporal video similarity learning"):
  with members:
    - "fivr fine grained incident video retrieval"
    - "video re localization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep multi task representation learning a tensor factorisation approach"
    - "deep residual learning for image recognition"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "coco cn for cross lingual image tagging captioning and retrieval"):
  with members:
    - "causally regularized learning with agnostic data selection bias"
    - "predicting visual features from text for image and video caption retrieval"
    - "imagination improves multimodal translation"
    - "using artificial tokens to control languages for multilingual image caption generation"
    - "ai challenger a large scale dataset for going deeper in image understanding"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "image pivoting for learning multilingual multimodal representations"
    - "stair captions constructing a large scale japanese image caption dataset"

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)