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: "tgif a new dataset and benchmark on animated gif description"
  Abstract: With the recent popularity of animated GIFs on social media, there is need for ways to index them with rich metadata. To advance research on animated GIF understanding, we collected a new dataset, Tumblr GIF (TGIF), with 100K animated GIFs from Tumblr and 120K natural language descriptions obtained via crowdsourcing. The motivation for this work is to develop a testbed for image sequence description systems, where the task is to generate natural language descriptions for animated GIFs or video clips. To ensure a high quality dataset, we developed a series of novel quality controls to validate free-form text input from crowdworkers. We show that there is unambiguous association between visual content and natural language descriptions in our dataset, making it an ideal benchmark for the visual content captioning task. We perform extensive statistical analyses to compare our dataset to existing image and video description datasets. Next, we provide baseline results on the animated GIF description task, using three representative techniques: nearest neighbor, statistical machine translation, and recurrent neural networks. Finally, we show that models fine-tuned from our animated GIF description dataset can be helpful for automatic movie description.

Connected through hyperedge HE-1 (induced by source paper "better captioning with sequence level exploration"):
  with members:
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "exploring visual relationship for image captioning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "video captioning with guidance of multimodal latent topics"

Connected through hyperedge HE-2 (induced by source paper "fine grained video text retrieval with hierarchical graph reasoning"):
  with members:
    - "visual semantic reasoning for image text matching"
    - "use what you have video retrieval using representations from collaborative experts"
    - "language conditioned graph networks for relational reasoning"
    - "simple bert models for relation extraction and semantic role labeling"
    - "evaluating text to image matching using binary image selection bison"
    - "relation aware graph attention network for visual question answering"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "graph r cnn for scene graph generation"

Connected through hyperedge HE-3 (induced by source paper "howto100m learning a text video embedding by watching hundred million narrated video clips"):
  with members:
    - "cross task weakly supervised learning from instructional videos"
    - "coin a large scale dataset for comprehensive instructional video analysis"
    - "scaling egocentric vision the epic kitchens dataset"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "unsupervised learning and segmentation of complex activities from video"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-4 (induced by source paper "vatex a large scale high quality multilingual dataset for video and language research"):
  with members:
    - "probing the need for visual context in multimodal machine translation"
    - "video description a survey of methods datasets and evaluation metrics"
    - "nocaps novel object captioning at scale"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "the memad submission to the wmt18 multimodal translation task"
    - "how2 a large scale dataset for multimodal language understanding"

Connected through hyperedge HE-5 (induced by source paper "polysemous visual semantic embedding for cross modal retrieval"):
  with members:
    - "on the convergence of adam and beyond"
    - "image2gif generating cinemagraphs using recurrent deep q networks"
    - "stacked cross attention for image text matching"
    - "cross modal retrieval with implicit concept association"
    - "modeling uncertainty with hedged instance embedding"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "attention is all you need"
    - "learning robust visual semantic embeddings"

Connected through hyperedge HE-6 (induced by source paper "streamlined dense video captioning"):
  with members:
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "move forward and tell a progressive generator of video descriptions"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"
    - "reconstruction network for video captioning"
    - "the activitynet large scale activity recognition challenge 2018 summary"
    - "attention is all you need"
    - "deep reinforcement learning based image captioning with embedding reward"

Connected through hyperedge HE-7 (induced by source paper "heterogeneous memory enhanced multimodal attention model for video question answering"):
  with members:
    - "motion appearance co memory networks for video question answering"
    - "a taxonomy for neural memory networks"
    - "tgif qa toward spatio temporal reasoning in visual question answering"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "visual question answering with memory augmented networks"
    - "multimodal residual learning for visual qa"
    - "end to end concept word detection for video captioning retrieval and question answering"
    - "leveraging video descriptions to learn video question answering"

Connected through hyperedge HE-8 (induced by source paper "object referring in videos with language and human gaze"):
  with members:
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "end to end representation learning for correlation filter based tracking"
    - "query guided regression network with context policy for phrase grounding"
    - "object referring in visual scene with spoken language"
    - "localizing moments in video with natural language"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"

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)