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: "bidirectional long short term memory for video description"
  Abstract: Video captioning has been attracting broad research attention in multimedia community. However, most existing approaches either ignore temporal information among video frames or just employ local contextual temporal knowledge. In this work, we propose a novel video captioning framework, termed as \emph{Bidirectional Long-Short Term Memory} (BiLSTM), which deeply captures bidirectional global temporal structure in video. Specifically, we first devise a joint visual modelling approach to encode video data by combining a forward LSTM pass, a backward LSTM pass, together with visual features from Convolutional Neural Networks (CNNs). Then, we inject the derived video representation into the subsequent language model for initialization. The benefits are in two folds: 1) comprehensively preserving sequential and visual information; and 2) adaptively learning dense visual features and sparse semantic representations for videos and sentences, respectively. We verify the effectiveness of our proposed video captioning framework on a commonly-used benchmark, i.e., Microsoft Video Description (MSVD) corpus, and the experimental results demonstrate that the superiority of the proposed approach as compared to several state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "low rank hoca efficient high order cross modal attention for video captioning"):
  with members:
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "reconstruction network for video captioning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "video captioning via hierarchical reinforcement learning"
    - "weakly supervised dense video captioning"
    - "video captioning with guidance of multimodal latent topics"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "a perceptual prediction framework for self supervised event segmentation"):
  with members:
    - "unsupervised learning and segmentation of complex activities from video"
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "predicting visual context for unsupervised event segmentation in continuous photo streams"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "connectionist temporal modeling for weakly supervised action labeling"
    - "temporal convolutional networks for action segmentation and detection"
    - "segmental spatiotemporal cnns for fine grained action segmentation"
    - "dynamic filter networks"

Connected through hyperedge HE-3 (induced by source paper "video based sign language recognition without temporal segmentation"):
  with members:
    - "jointly modeling embedding and translation to bridge video and language"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "sequence to sequence video to text"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "translating videos to natural language using deep recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "recurrent image captioner describing images with spatial invariant transformation and attention filtering"):
  with members:
    - "microsoft coco captions data collection and evaluation server"
    - "neural machine translation by jointly learning to align and translate"
    - "learning a recurrent visual representation for image caption generation"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-5 (induced by source paper "binary subspace coding for query by image video retrieval"):
  with members:
    - "a discriminative cnn video representation for event detection"

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.MM(Multimedia)