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: "improving lstm based video description with linguistic knowledge mined from text"
  Abstract: This paper investigates how linguistic knowledge mined from large text corpora can aid the generation of natural language descriptions of videos. Specifically, we integrate both a neural language model and distributional semantics trained on large text corpora into a recent LSTM-based architecture for video description. We evaluate our approach on a collection of Youtube videos as well as two large movie description datasets showing significant improvements in grammaticality while modestly improving descriptive quality.

Connected through hyperedge HE-1 (induced by source paper "augmenting neural machine translation with knowledge graphs"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "learning beyond datasets knowledge graph augmented neural networks for natural language processing"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "machine translation using semantic web technologies a survey"
    - "an evaluation of neural machine translation models on historical spelling normalization"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "learning word vectors for 157 languages"
    - "the importance of being recurrent for modeling hierarchical structure"

Connected through hyperedge HE-2 (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"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "multimodal emotion classification"):
  with members:
    - "what s ur type contextualized classification of user types in marijuana related communications using compositional multiview embedding"
    - "which emoji talks best for my picture"
    - "learning emoji embeddings using emoji co occurrence network graph"
    - "multimodal emoji prediction"
    - "a semantics based measure of emoji similarity"
    - "fasttext zip compressing text classification models"
    - "emojinet an open service and api for emoji sense discovery"
    - "finding street gang members on twitter"

Connected through hyperedge HE-4 (induced by source paper "visual discourse parsing"):
  with members:
    - "multi task video captioning with video and entailment generation"
    - "top down visual saliency guided by captions"
    - "video paragraph captioning using hierarchical recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "polite dialogue generation without parallel data"):
  with members:
    - "style transfer from non parallel text by cross alignment"
    - "controlling linguistic style aspects in neural language generation"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "unsupervised image to image translation networks"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "steering output style and topic in neural response generation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "end to end video captioning with multitask reinforcement learning"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "weakly supervised dense video captioning"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "multitask learning with low level auxiliary tasks for encoder decoder based speech recognition"

Connected through hyperedge HE-7 (induced by source paper "semstyle learning to generate stylised image captions using unaligned text"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "controlling linguistic style aspects in neural language generation"
    - "areas of attention for image captioning"
    - "spice semantic propositional image caption evaluation"
    - "boosting image captioning with attributes"
    - "phi lstm a phrase based hierarchical lstm model for image captioning"
    - "captioning images with diverse objects"

Connected through hyperedge HE-8 (induced by source paper "learning to compose topic aware mixture of experts for zero shot video captioning"):
  with members:
    - "neural baby talk"
    - "universal neural machine translation for extremely low resource languages"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "s3d single shot multi span detector via fully 3d convolutional networks"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "tafe net task aware feature embeddings for efficient learning and inference"
    - "decoupled novel object captioner"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"

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.CL(Computation and Language)