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: "senticap generating image descriptions with sentiments"
  Abstract: The recent progress on image recognition and language modeling is making automatic description of image content a reality. However, stylized, non-factual aspects of the written description are missing from the current systems. One such style is descriptions with emotions, which is commonplace in everyday communication, and influences decision-making and interpersonal relationships. We design a system to describe an image with emotions, and present a model that automatically generates captions with positive or negative sentiments. We propose a novel switching recurrent neural network with word-level regularization, which is able to produce emotional image captions using only 2000+ training sentences containing sentiments. We evaluate the captions with different automatic and crowd-sourcing metrics. Our model compares favourably in common quality metrics for image captioning. In 84.6% of cases the generated positive captions were judged as being at least as descriptive as the factual captions. Of these positive captions 88% were confirmed by the crowd-sourced workers as having the appropriate sentiment.

Connected through hyperedge HE-1 (induced by source paper "aitpr attribute interaction tensor product representation for image caption"):
  with members:
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "learning to guide decoding for image captioning"
    - "neural baby talk"
    - "coco cn for cross lingual image tagging captioning and retrieval"
    - "image captioning at will a versatile scheme for effectively injecting sentiments into image descriptions"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "multimodal image captioning for marketing analysis"
    - "pragmatically informative image captioning with character level reference"

Connected through hyperedge HE-2 (induced by source paper "mrrc multiple role representation crossover interpretation for image captioning with r cnn feature distribution composition fdc"):
  with members:
    - "learning to guide decoding for image captioning"
    - "neural baby talk"
    - "coco cn for cross lingual image tagging captioning and retrieval"
    - "image captioning at will a versatile scheme for effectively injecting sentiments into image descriptions"
    - "attentive tensor product learning"
    - "pragmatically informative image captioning with character level reference"
    - "adversarial semantic alignment for improved image captions"
    - "entity aware image caption generation"

Connected through hyperedge HE-3 (induced by source paper "say as you wish fine grained control of image caption generation with abstract scene graphs"):
  with members:
    - "graphical contrastive losses for scene graph parsing"
    - "sequential latent spaces for modeling the intention during diverse image captioning"
    - "ctrl a conditional transformer language model for controllable generation"
    - "robust change captioning"
    - "describing like humans on diversity in image captioning"
    - "nocaps novel object captioning at scale"
    - "intention oriented image captions with guiding objects"
    - "neural baby talk"

Connected through hyperedge HE-4 (induced by source paper "gaussian smoothen semantic features gssf exploring the linguistic aspects of visual captioning in indian languages bengali using mscoco framework"):
  with members:
    - "neural baby talk"
    - "image captioning at will a versatile scheme for effectively injecting sentiments into image descriptions"
    - "pragmatically informative image captioning with character level reference"
    - "entity aware image caption generation"
    - "joint image captioning and question answering"
    - "improved image captioning via policy gradient optimization of spider"
    - "actor critic sequence training for image captioning"
    - "deep reinforcement learning based image captioning with embedding reward"

Connected through hyperedge HE-5 (induced by source paper "image captioning using facial expression and attention"):
  with members:
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "face cap image captioning using facial expression analysis"
    - "automatic recognition of student engagement using deep learning and facial expression"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "image captioning at will a versatile scheme for effectively injecting sentiments into image descriptions"
    - "a comprehensive survey of deep learning for image captioning"
    - "learning to generate reviews and discovering sentiment"
    - "toward controlled generation of text"

Connected through hyperedge HE-6 (induced by source paper "deep unified multimodal embeddings for understanding both content and users in social media networks"):
  with members:
    - "integrating text and image determining multimodal document intent in instagram posts"
    - "align2ground weakly supervised phrase grounding guided by image caption alignment"
    - "universal dependency parsing from scratch"
    - "deep learning based recommender system a survey and new perspectives"
    - "exploring the limits of weakly supervised pretraining"
    - "a comprehensive survey of deep learning for image captioning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attributed social network embedding"

Connected through hyperedge HE-7 (induced by source paper "crur coupled recurrent unit for unification conceptualization and context capture for language representation a generalization of bi directional lstm"):
  with members:
    - "learning to guide decoding for image captioning"
    - "neural baby talk"
    - "image captioning at will a versatile scheme for effectively injecting sentiments into image descriptions"
    - "pragmatically informative image captioning with character level reference"
    - "adversarial semantic alignment for improved image captions"
    - "entity aware image caption generation"
    - "joint image captioning and question answering"
    - "improved image captioning via policy gradient optimization of spider"

Connected through hyperedge HE-8 (induced by source paper "adapting language models for non parallel author stylized rewriting"):
  with members:
    - "a lexical syntactic and semantic perspective for understanding style in text"
    - "mass masked sequence to sequence pre training for language generation"
    - "evaluating style transfer for text"
    - "cross lingual language model pretraining"
    - "multiple attribute text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "polite dialogue generation without parallel data"

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)