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: "memory attended recurrent network for video captioning"
  Abstract: Typical techniques for video captioning follow the encoder-decoder framework, which can only focus on one source video being processed. A potential disadvantage of such design is that it cannot capture the multiple visual context information of a word appearing in more than one relevant videos in training data. To tackle this limitation, we propose the Memory-Attended Recurrent Network (MARN) for video captioning, in which a memory structure is designed to explore the full-spectrum correspondence between a word and its various similar visual contexts across videos in training data. Thus, our model is able to achieve a more comprehensive understanding for each word and yield higher captioning quality. Furthermore, the built memory structure enables our method to model the compatibility between adjacent words explicitly instead of asking the model to learn implicitly, as most existing models do. Extensive validation on two real-word datasets demonstrates that our MARN consistently outperforms state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "multi modal dense video captioning"):
  with members:
    - "watch listen and tell multi modal weakly supervised dense event captioning"
    - "a case study on combining asr and visual features for generating instructional video captions"
    - "streamlined dense video captioning"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "weakly supervised dense event captioning in videos"
    - "move forward and tell a progressive generator of video descriptions"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"

Connected through hyperedge HE-2 (induced by source paper "object relational graph with teacher recommended learning for video captioning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "controllable video captioning with pos sequence guidance based on gated fusion network"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "object aware aggregation with bidirectional temporal graph for video captioning"
    - "relation aware graph attention network for visual question answering"
    - "less is more picking informative frames for video captioning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "videos as space time region graphs"

Connected through hyperedge HE-3 (induced by source paper "non autoregressive video captioning with iterative refinement"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "temporal deformable convolutional encoder decoder networks for video captioning"
    - "mask predict parallel decoding of conditional masked language models"
    - "a neural compositional paradigm for image captioning"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "reconstruction network for video captioning"

Connected through hyperedge HE-4 (induced by source paper "cpgan full spectrum content parsing generative adversarial networks for text to image synthesis"):
  with members:
    - "object driven text to image synthesis via adversarial training"
    - "mirrorgan learning text to image generation by redescription"
    - "dm gan dynamic memory generative adversarial networks for text to image synthesis"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "automatic stance detection using end to end memory networks"
    - "yolov3 an incremental improvement"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-5 (induced by source paper "video captioning with text based dynamic attention and step by step learning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "object aware aggregation with bidirectional temporal graph for video captioning"
    - "reconstruction network for video captioning"
    - "end to end video captioning with multitask reinforcement learning"
    - "saliency guided video classification via adaptively weighted learning"
    - "hierarchical lstm with adjusted temporal attention for video captioning"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "two stream collaborative learning with spatial temporal attention for video classification"

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)