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: "automatic spatially aware fashion concept discovery"
  Abstract: This paper proposes an automatic spatially-aware concept discovery approach using weakly labeled image-text data from shopping websites. We first fine-tune GoogleNet by jointly modeling clothing images and their corresponding descriptions in a visual-semantic embedding space. Then, for each attribute (word), we generate its spatially-aware representation by combining its semantic word vector representation with its spatial representation derived from the convolutional maps of the fine-tuned network. The resulting spatially-aware representations are further used to cluster attributes into multiple groups to form spatially-aware concepts (e.g., the neckline concept might consist of attributes like v-neck, round-neck, etc). Finally, we decompose the visual-semantic embedding space into multiple concept-specific subspaces, which facilitates structured browsing and attribute-feedback product retrieval by exploiting multimodal linguistic regularities. We conducted extensive experiments on our newly collected Fashion200K dataset, and results on clustering quality evaluation and attribute-feedback product retrieval task demonstrate the effectiveness of our automatically discovered spatially-aware concepts.

Connected through hyperedge HE-1 (induced by source paper "fine grained fashion similarity learning by attribute specific embedding network"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "cbam convolutional block attention module"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "clothing retrieval with visual attention model"
    - "attention is all you need"
    - "learning fashion compatibility with bidirectional lstms"
    - "exploring human like attention supervision in visual question answering"

Connected through hyperedge HE-2 (induced by source paper "fashion retrieval via graph reasoning networks on a similarity pyramid"):
  with members:
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "netizen style commenting on fashion photos dataset and diversity measures"
    - "end to end deep kronecker product matching for person re identification"
    - "video2shop exactly matching clothes in videos to online shopping images"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "deep randomized ensembles for metric learning"
    - "improving the annotation of deepfashion images for fine grained attribute recognition"
    - "person re identification with deep similarity guided graph neural network"

Connected through hyperedge HE-3 (induced by source paper "why do these match explaining the behavior of image similarity models"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "learning type aware embeddings for fashion compatibility"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "discriminative learning of latent features for zero shot recognition"
    - "a zero shot framework for sketch based image retrieval"

Connected through hyperedge HE-4 (induced by source paper "layout graph reasoning for fashion landmark detection"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "hierarchical graph representation learning with differentiable pooling"
    - "relational inductive biases deep learning and graph networks"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "learning feature pyramids for human pose estimation"
    - "unconstrained fashion landmark detection via hierarchical recurrent transformer networks"

Connected through hyperedge HE-5 (induced by source paper "cooperative embeddings for instance attribute and category retrieval"):
  with members:
    - "a modulation module for multi task learning with applications in image retrieval"
    - "taskonomy disentangling task transfer learning"
    - "improving the annotation of deepfashion images for fine grained attribute recognition"
    - "in defense of the triplet loss for person re identification"
    - "no fuss distance metric learning using proxies"
    - "learning fashion compatibility with bidirectional lstms"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "an overview of multi task learning in deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "let s transfer transformations of shared semantic representations"):
  with members:
    - "generalization in metric learning should the embedding layer be the embedding layer"
    - "composing text and image for image retrieval an empirical odyssey"
    - "text2shape generating shapes from natural language by learning joint embeddings"
    - "image generation from scene graphs"
    - "dialog based interactive image retrieval"
    - "texturegan controlling deep image synthesis with texture patches"
    - "adversarial discriminative domain adaptation"
    - "scene graph generation by iterative message passing"

Connected through hyperedge HE-7 (induced by source paper "snap and find deep discrete cross domain garment image retrieval"):
  with members:
    - "hashnet deep learning to hash by continuation"
    - "cross domain image retrieval with attention modeling"
    - "video captioning with transferred semantic attributes"
    - "cp mtml coupled projection multi task metric learning for large scale face retrieval"
    - "a survey on learning to hash"
    - "boosting image captioning with attributes"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-8 (induced by source paper "complete the look scene based complementary product recommendation"):
  with members:
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "do better imagenet models transfer better"
    - "video2shop exactly matching clothes in videos to online shopping images"
    - "learning type aware embeddings for fashion compatibility"
    - "recommending outfits from personal closet"
    - "creating capsule wardrobes from fashion images"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "compatibility family learning for item recommendation and generation"

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)