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: "category based deep cca for fine grained venue discovery from multimodal data"
  Abstract: In this work, travel destination and business location are taken as venues. Discovering a venue by a photo is very important for context-aware applications. Unfortunately, few efforts paid attention to complicated real images such as venue photos generated by users. Our goal is fine-grained venue discovery from heterogeneous social multimodal data. To this end, we propose a novel deep learning model, Category-based Deep Canonical Correlation Analysis (C-DCCA). Given a photo as input, this model performs (i) exact venue search (find the venue where the photo was taken), and (ii) group venue search (find relevant venues with the same category as that of the photo), by the cross-modal correlation between the input photo and textual description of venues. In this model, data in different modalities are projected to a same space via deep networks. Pairwise correlation (between different modal data from the same venue) for exact venue search and category-based correlation (between different modal data from different venues with the same category) for group venue search are jointly optimized. Because a photo cannot fully reflect rich text description of a venue, the number of photos per venue in the training phase is increased to capture more aspects of a venue. We build a new venue-aware multimodal dataset by integrating Wikipedia featured articles and Foursquare venue photos. Experimental results on this dataset confirm the feasibility of the proposed method. Moreover, the evaluation over another publicly available dataset confirms that the proposed method outperforms state-of-the-arts for cross-modal retrieval between image and text.

Connected through hyperedge HE-1 (induced by source paper "video face super resolution with motion adaptive feedback cell"):
  with members:
    - "fast spatio temporal residual network for video super resolution"
    - "recurrent back projection network for video super resolution"
    - "frame recurrent video super resolution"
    - "cosface large margin cosine loss for deep face recognition"
    - "super identity convolutional neural network for face hallucination"
    - "residual dense network for image super resolution"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "deep cross modal correlation learning for audio and lyrics in music retrieval"

Connected through hyperedge HE-2 (induced by source paper "text2facegan face generation from fine grained textual descriptions"):
  with members:
    - "wav2pix speech conditioned face generation using generative adversarial networks"
    - "face cap image captioning using facial expression analysis"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "face2text collecting an annotated image description corpus for the generation of rich face descriptions"
    - "a style based generator architecture for generative adversarial networks"
    - "deep cross modal correlation learning for audio and lyrics in music retrieval"
    - "improved techniques for training gans"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-3 (induced by source paper "audio visual embedding for cross modal musicvideo retrieval through supervised deep cca"):
  with members:
    - "deep cross modal correlation learning for audio and lyrics in music retrieval"
    - "cnn architectures for large scale audio classification"
    - "end to end concept word detection for video captioning retrieval and question answering"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "youtube 8m a large scale video classification benchmark"
    - "deep cross modal hashing"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "learning joint embedding for cross modal retrieval"):
  with members:
    - "deep triplet neural networks with cluster cca for audio visual cross modal retrieval"
    - "deep cross modal correlation learning for audio and lyrics in music retrieval"
    - "learning from between class examples for deep sound recognition"
    - "learnable pooling with context gating for video classification"
    - "visual to sound generating natural sound for videos in the wild"

Connected through hyperedge HE-5 (induced by source paper "deep learning of human perception in audio event classification"):
  with members:
    - "cnn architectures for large scale audio classification"
    - "densenet implementing efficient convnet descriptor pyramids"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "deep cross modal correlation learning for audio and lyrics in music retrieval"):
  with members:
    - "lyrics based music genre classification using a hierarchical attention network"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"
    - "instance aware image and sentence matching with selective multimodal lstm"
    - "cnn architectures for large scale audio classification"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "multi view recurrent neural acoustic word embeddings"
    - "automatic tagging using deep convolutional neural networks"
    - "deep cross modal 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.CV(Computer Vision and Pattern Recognition)