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: "the long short story of movie description"
  Abstract: Generating descriptions for videos has many applications including assisting blind people and human-robot interaction. The recent advances in image captioning as well as the release of large-scale movie description datasets such as MPII Movie Description allow to study this task in more depth. Many of the proposed methods for image captioning rely on pre-trained object classifier CNNs and Long-Short Term Memory recurrent networks (LSTMs) for generating descriptions. While image description focuses on objects, we argue that it is important to distinguish verbs, objects, and places in the challenging setting of movie description. In this work we show how to learn robust visual classifiers from the weak annotations of the sentence descriptions. Based on these visual classifiers we learn how to generate a description using an LSTM. We explore different design choices to build and train the LSTM and achieve the best performance to date on the challenging MPII-MD dataset. We compare and analyze our approach and prior work along various dimensions to better understand the key challenges of the movie description task.

Connected through hyperedge HE-1 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning"):
  with members:
    - "end to end learning of visual representations from uncurated instructional videos"
    - "use what you have video retrieval using representations from collaborative experts"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "videobert a joint model for video and language representation learning"
    - "grounding object detections with transcriptions"
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "learning to detect and retrieve objects from unlabeled videos"

Connected through hyperedge HE-2 (induced by source paper "captioning near future activity sequences"):
  with members:
    - "move forward and tell a progressive generator of video descriptions"
    - "when will you do what anticipating temporal occurrences of activities"
    - "dense captioning events in videos"
    - "estimating human intent for physical human robot co manipulation"
    - "towards automatic learning of procedures from web instructional videos"
    - "recurrent topic transition gan for visual paragraph generation"
    - "spice semantic propositional image caption evaluation"
    - "first person activity forecasting with online inverse reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "m vad names a dataset for video captioning with naming"):
  with members:
    - "end to end face detection and cast grouping in movies using erd h o s r e nyi clustering"
    - "generating descriptions with grounded and co referenced people"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "moviegraphs towards understanding human centric situations from videos"
    - "dense captioning events in videos"
    - "learning from video and text via large scale discriminative clustering"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "improving lstm based video description with linguistic knowledge mined from text"

Connected through hyperedge HE-4 (induced by source paper "adversarial inference for multi sentence video description"):
  with members:
    - "video to video synthesis"
    - "grounding visual explanations"
    - "less is more picking informative frames for video captioning"
    - "improving image captioning with conditional generative adversarial nets"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "improving reinforcement learning based image captioning with natural language prior"
    - "learning to write with cooperative discriminators"
    - "bidirectional attentive fusion with context gating for dense video captioning"

Connected through hyperedge HE-5 (induced by source paper "a joint sequence fusion model for video question answering and retrieval"):
  with members:
    - "video fill in the blank using lr rl lstms with spatial temporal attentions"
    - "learning from video and text via large scale discriminative clustering"
    - "tgif qa toward spatio temporal reasoning in visual question answering"
    - "hadamard product for low rank bilinear pooling"
    - "a dataset and exploration of models for understanding video data through fill in the blank question answering"
    - "learning language visual embedding for movie understanding with natural language"
    - "cnn architectures for large scale audio classification"
    - "end to end concept word detection for video captioning retrieval and question answering"

Connected through hyperedge HE-6 (induced by source paper "video captioning with boundary aware hierarchical language decoding and joint video prediction"):
  with members:
    - "hierarchical lstm with adjusted temporal attention for video captioning"
    - "from deterministic to generative multi modal stochastic rnns for video captioning"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "multi task video captioning with video and entailment generation"
    - "video captioning with transferred semantic attributes"
    - "semantic compositional networks for visual captioning"
    - "jointly modeling embedding and translation to bridge video and language"
    - "hierarchical recurrent neural encoder for video representation with application to captioning"

Connected through hyperedge HE-7 (induced by source paper "move forward and tell a progressive generator of video descriptions"):
  with members:
    - "texygen a benchmarking platform for text generation models"
    - "temporal action detection with structured segment networks"
    - "detecting visual relationships with deep relational networks"
    - "dense captioning events in videos"
    - "contrastive learning for image captioning"
    - "attention based multimodal fusion for video description"
    - "weakly supervised dense video captioning"
    - "hierarchical boundary aware neural encoder for video captioning"

Connected through hyperedge HE-8 (induced by source paper "mtle a multitask learning encoder of visual feature representations for video and movie description"):
  with members:
    - "consistent multitask learning with nonlinear output relations"
    - "multi task video captioning with video and entailment generation"
    - "video captioning with transferred semantic attributes"
    - "semantic compositional networks for visual captioning"
    - "improving lstm based video description with linguistic knowledge mined from text"
    - "vqa visual question answering"
    - "skip thought vectors"
    - "a dataset for movie description"

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)