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: "outfit generation and style extraction via bidirectional lstm and autoencoder"
  Abstract: When creating an outfit, style is a criterion in selecting each fashion item. This means that style can be regarded as a feature of the overall outfit. However, in various previous studies on outfit generation, there have been few methods focusing on global information obtained from an outfit. To address this deficiency, we have incorporated an unsupervised style extraction module into a model to learn outfits. Using the style information of an outfit as a whole, the proposed model succeeded in generating outfits more flexibly without requiring additional information. Moreover, the style information extracted by the proposed model is easy to interpret. The proposed model was evaluated on two human-generated outfit datasets. In a fashion item prediction task (missing prediction task), the proposed model outperformed a baseline method. In a style extraction task, the proposed model extracted some easily distinguishable styles. In an outfit generation task, the proposed model generated an outfit while controlling its styles. This capability allows us to generate fashionable outfits according to various preferences.

Connected through hyperedge HE-1 (induced by source paper "machine learning based co creative design framework"):
  with members:
    - "friend collaborator student manager how design of an ai driven game level editor affects creators"
    - "toward a neuro inspired creative decoder"
    - "design design inspiration from generative networks"
    - "co creative level design via machine learning"
    - "styling with attention to details"
    - "generative image inpainting with contextual attention"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-2 (induced by source paper "context aware visual compatibility prediction"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "learning type aware embeddings for fashion compatibility"
    - "relational inductive biases deep learning and graph networks"
    - "recommending outfits from personal closet"
    - "prototypical networks for few shot learning"
    - "creating capsule wardrobes from fashion images"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "compatibility family learning for item recommendation and generation"

Connected through hyperedge HE-3 (induced by source paper "improving outfit recommendation with co supervision of fashion generation"):
  with members:
    - "aesthetic based clothing recommendation"
    - "neural compatibility modeling with attentive knowledge distillation"
    - "creating capsule wardrobes from fashion images"
    - "visually aware fashion recommendation and design with generative image models"
    - "learning fashion compatibility with bidirectional lstms"
    - "cvae gan fine grained image generation through asymmetric training"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"
    - "neural attentive session based recommendation"

Connected through hyperedge HE-4 (induced by source paper "outfit compatibility prediction and diagnosis with multi layered comparison network"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "recommending outfits from personal closet"
    - "non local neural networks"
    - "attention is all you need"
    - "compatibility family learning for item recommendation and generation"
    - "style2vec representation learning for fashion items from style sets"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "learning fashion compatibility with bidirectional lstms"

Connected through hyperedge HE-5 (induced by source paper "pog personalized outfit generation for fashion recommendation at alibaba ifashion"):
  with members:
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "learning type aware embeddings for fashion compatibility"
    - "deep contextualized word representations"
    - "deepstyle multimodal search engine for fashion and interior design"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "self attentive sequential recommendation"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "neural compatibility modeling with attentive knowledge distillation"

Connected through hyperedge HE-6 (induced by source paper "deep set to set matching and learning"):
  with members:
    - "group reidentification with multigrained matching and integration"
    - "dressing as a whole outfit compatibility learning based on node wise graph neural networks"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "learning type aware embeddings for fashion compatibility"
    - "deep sets"
    - "recommending outfits from personal closet"
    - "fashionnet personalized outfit recommendation with deep neural network"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-7 (induced by source paper "fashion outfit generation for e commerce"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "recommending outfits from personal closet"
    - "fashionnet personalized outfit recommendation with deep neural network"
    - "neural compatibility modeling with attentive knowledge distillation"
    - "class weighted convolutional features for visual instance search"
    - "compatibility family learning for item recommendation and generation"
    - "learning fashion compatibility with bidirectional lstms"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"

Connected through hyperedge HE-8 (induced by source paper "theme matters fashion compatibility learning via theme attention"):
  with members:
    - "fashion outfit generation for e commerce"
    - "learning type aware embeddings for fashion compatibility"
    - "recommending outfits from personal closet"
    - "creating capsule wardrobes from fashion images"
    - "compatibility family learning for item recommendation and generation"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "learning fashion compatibility with bidirectional lstms"
    - "mining fashion outfit composition using an end to end deep learning approach on set 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)