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: "a multiplicative model for learning distributed text based attribute representations"
  Abstract: In this paper we propose a general framework for learning distributed representations of attributes: characteristics of text whose representations can be jointly learned with word embeddings. Attributes can correspond to document indicators (to learn sentence vectors), language indicators (to learn distributed language representations), meta-data and side information (such as the age, gender and industry of a blogger) or representations of authors. We describe a third-order model where word context and attribute vectors interact multiplicatively to predict the next word in a sequence. This leads to the notion of conditional word similarity: how meanings of words change when conditioned on different attributes. We perform several experimental tasks including sentiment classification, cross-lingual document classification, and blog authorship attribution. We also qualitatively evaluate conditional word neighbours and attribute-conditioned text generation.

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 "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-4 (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-5 (induced by source paper "learning embeddings into entropic wasserstein spaces"):
  with members:
    - "stochastic wasserstein barycenters"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "context mover s distance barycenters optimal transport of contexts for building representations"
    - "deep contextualized word representations"
    - "imagenet pre trained models with batch normalization"
    - "node2vec scalable feature learning for networks"
    - "enriching word vectors with subword information"
    - "learning with a wasserstein loss"

Connected through hyperedge HE-6 (induced by source paper "content preserving text generation with attribute controls"):
  with members:
    - "cgans with projection discriminator"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "learning to generate reviews and discovering sentiment"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "unsupervised machine translation using monolingual corpora only"
    - "a 4 nt author attribute anonymity by adversarial training of neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "contextual sequence modeling for recommendation with recurrent neural networks"):
  with members:
    - "syntax aware neural machine translation using ccg"
    - "meta prod2vec product embeddings using side information for recommendation"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "general factorization framework for context aware recommendations"
    - "architectural complexity measures of recurrent neural networks"
    - "linguistic input features improve neural machine translation"
    - "on multiplicative integration with recurrent neural networks"
    - "session based recommendations with recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "semantic compositional networks for visual captioning"):
  with members:
    - "review networks for caption generation"
    - "image captioning with semantic attention"
    - "attention correctness in neural image captioning"
    - "rich image captioning in the wild"
    - "adaptive feature abstraction for translating video to text"
    - "adaptive feature abstraction for translating video to language"
    - "on multiplicative integration with recurrent neural networks"
    - "learning attributes equals multi source domain generalization"

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.LG(Machine Learning)