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: "triplet based deep hashing network for cross modal retrieval"
  Abstract: Given the benefits of its low storage requirements and high retrieval efficiency, hashing has recently received increasing attention. In particular,cross-modal hashing has been widely and successfully used in multimedia similarity search applications. However, almost all existing methods employing cross-modal hashing cannot obtain powerful hash codes due to their ignoring the relative similarity between heterogeneous data that contains richer semantic information, leading to unsatisfactory retrieval performance. In this paper, we propose a triplet-based deep hashing (TDH) network for cross-modal retrieval. First, we utilize the triplet labels, which describes the relative relationships among three instances as supervision in order to capture more general semantic correlations between cross-modal instances. We then establish a loss function from the inter-modal view and the intra-modal view to boost the discriminative abilities of the hash codes. Finally, graph regularization is introduced into our proposed TDH method to preserve the original semantic similarity between hash codes in Hamming space. Experimental results show that our proposed method outperforms several state-of-the-art approaches on two popular cross-modal datasets.

Connected through hyperedge HE-1 (induced by source paper "university 1652 a multi view multi source benchmark for drone based geo localization"):
  with members:
    - "building information modeling and classification by visual learning at a city scale"
    - "lending orientation to neural networks for cross view geo localization"
    - "optimal feature transport for cross view image geo localization"
    - "vision meets drones a challenge"
    - "stochastic attraction repulsion embedding for large scale image localization"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "cross view image matching for geo localization in urban environments"

Connected through hyperedge HE-2 (induced by source paper "revisiting embodiedqa a simple baseline and beyond"):
  with members:
    - "multi target embodied question answering"
    - "active transfer learning network a unified deep joint spectral spatial feature learning model for hyperspectral image classification"
    - "interpretable visual question answering by visual grounding from attention supervision mining"
    - "blindfold baselines for embodied qa"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "neural modular control for embodied question answering"
    - "a better way to attend attention with trees for video question answering"
    - "show ask attend and answer a strong baseline for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "stacked semantic guided network for zero shot sketch based image retrieval"):
  with members:
    - "shared predictive cross modal deep quantization"
    - "zero shot sketch image hashing"
    - "a zero shot framework for sketch based image retrieval"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "semantic autoencoder for zero shot learning"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "deep sketch hashing fast free hand sketch based image retrieval"

Connected through hyperedge HE-4 (induced by source paper "deep supervised hashing leveraging quadratic spherical mutual information for content based image retrieval"):
  with members:
    - "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"
    - "a survey on learning to hash"

Connected through hyperedge HE-5 (induced by source paper "distillhash unsupervised deep hashing by distilling data pairs"):
  with members:
    - "fast supervised discrete hashing"
    - "shared predictive cross modal deep quantization"
    - "coupled cyclegan unsupervised hashing network for cross modal retrieval"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "zero shot sketch image hashing"
    - "evolutionary generative adversarial networks"
    - "masking a new perspective of noisy supervision"
    - "self supervised adversarial hashing networks for cross modal retrieval"

Connected through hyperedge HE-6 (induced by source paper "asymmetric deep semantic quantization for image retrieval"):
  with members:
    - "deep attention guided hashing"
    - "deep ordinal hashing with spatial attention"
    - "deep priority hashing"
    - "deep discrete supervised hashing"
    - "deep supervised discrete hashing"
    - "asymmetric deep supervised hashing"
    - "a survey on learning to hash"
    - "deep cross modal hashing"

Connected through hyperedge HE-7 (induced by source paper "semantic adversarial network with multi scale pyramid attention for video classification"):
  with members:
    - "shared predictive cross modal deep quantization"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "compressed video action recognition"
    - "learning long term dependencies for action recognition with a biologically inspired deep network"
    - "temporal convolutional networks for action segmentation and detection"

Connected through hyperedge HE-8 (induced by source paper "analysis of evolutionary behavior in self learning media search engines"):
  with members:
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "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"

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)