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: "video2shop exactly matching clothes in videos to online shopping images"
  Abstract: In recent years, both online retail and video hosting service are exponentially growing. In this paper, we explore a new cross-domain task, Video2Shop, targeting for matching clothes appeared in videos to the exact same items in online shops. A novel deep neural network, called AsymNet, is proposed to explore this problem. For the image side, well-established methods are used to detect and extract features for clothing patches with arbitrary sizes. For the video side, deep visual features are extracted from detected object regions in each frame, and further fed into a Long Short-Term Memory (LSTM) framework for sequence modeling, which captures the temporal dynamics in videos. To conduct exact matching between videos and online shopping images, LSTM hidden states, representing the video, and image features, which represent static object images, are jointly modeled under the similarity network with reconfigurable deep tree structure. Moreover, an approximate training method is proposed to achieve the efficiency when training. Extensive experiments conducted on a large cross-domain dataset have demonstrated the effectiveness and efficiency of the proposed AsymNet, which outperforms the state-of-the-art methods.

Connected through hyperedge HE-1 (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"
    - "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"
    - "videos as space time region graphs"

Connected through hyperedge HE-2 (induced by source paper "improving the learning of multi column convolutional neural network for crowd counting"):
  with members:
    - "learning spatial awareness to improve crowd counting"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "stacked pooling improving crowd counting by boosting scale invariance"
    - "in defense of single column networks for crowd counting"
    - "context aware crowd counting"
    - "adaptive scenario discovery for crowd counting"
    - "padnet pan density crowd counting"
    - "top down feedback for crowd counting convolutional neural network"

Connected through hyperedge HE-3 (induced by source paper "learning fashion compatibility across apparel categories for outfit recommendation"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "neural compatibility modeling with attentive knowledge distillation"
    - "creating capsule wardrobes from fashion images"
    - "exploiting restricted boltzmann machines and deep belief networks in compressed sensing"
    - "learning fashion compatibility with bidirectional lstms"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"
    - "learning compatibility across categories for heterogeneous item recommendation"
    - "multi task curriculum transfer deep learning of clothing attributes"

Connected through hyperedge HE-4 (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"
    - "learning type aware embeddings for fashion compatibility"
    - "recommending outfits from personal closet"
    - "creating capsule wardrobes from fashion images"
    - "automatic spatially aware fashion concept discovery"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "compatibility family learning for item recommendation and generation"

Connected through hyperedge HE-5 (induced by source paper "one shot segmentation in clutter"):
  with members:
    - "unsupervised deep object discovery for instance recognition"
    - "deep extreme cut from extreme points to object segmentation"
    - "prototypical networks for few shot learning"
    - "webvision challenge visual learning and understanding with web data"
    - "one shot joint colocalization and cosegmentation"
    - "attentive recurrent comparators"
    - "rethinking atrous convolution for semantic image segmentation"
    - "few shot learning through an information retrieval lens"

Connected through hyperedge HE-6 (induced by source paper "a deep learning based fashion attributes detection model"):
  with members:
    - "streetstyle exploring world wide clothing styles from millions of photos"
    - "looking at outfit to parse clothing"
    - "cross domain image retrieval with attention modeling"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "multi task curriculum transfer deep learning of clothing attributes"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "clothing co parsing by joint image segmentation and labeling"

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)