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: "multi task curriculum transfer deep learning of clothing attributes"
  Abstract: Recognising detailed clothing characteristics (fine-grained attributes) in unconstrained images of people in-the-wild is a challenging task for computer vision, especially when there is only limited training data from the wild whilst most data available for model learning are captured in well-controlled environments using fashion models (well lit, no background clutter, frontal view, high-resolution). In this work, we develop a deep learning framework capable of model transfer learning from well-controlled shop clothing images collected from web retailers to in-the-wild images from the street. Specifically, we formulate a novel Multi-Task Curriculum Transfer (MTCT) deep learning method to explore multiple sources of different types of web annotations with multi-labelled fine-grained attributes. Our multi-task loss function is designed to extract more discriminative representations in training by jointly learning all attributes, and our curriculum strategy exploits the staged easy-to-complex transfer learning motivated by cognitive studies. We demonstrate the advantages of the MTCT model over the state-of-the-art methods on the X-Domain benchmark, a large scale clothing attribute dataset. Moreover, we show that the MTCT model has a notable advantage over contemporary models when the training data size is small.

Connected through hyperedge HE-1 (induced by source paper "pedestrian attribute recognition a survey"):
  with members:
    - "the deeper the better analysis of person attributes recognition"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "improving person re identification by attribute and identity learning"
    - "amc automl for model compression and acceleration on mobile devices"
    - "rgb t object tracking benchmark and baseline"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "describe and attend to track learning natural language guided structural representation and visual attention for object tracking"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-2 (induced by source paper "unsupervised tracklet person re identification"):
  with members:
    - "multi level factorisation net for person re identification"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "deep association learning for unsupervised video person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "domain adaptation through synthesis for unsupervised person re identification"
    - "unsupervised person re identification by deep learning tracklet association"
    - "image image domain adaptation with preserved self similarity and domain dissimilarity for person re identification"
    - "person re identification by deep joint learning of multi loss classification"

Connected through hyperedge HE-3 (induced by source paper "unsupervised deep learning by neighbourhood discovery"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep clustering for unsupervised learning of visual features"
    - "single label multi class image classification by deep logistic regression"
    - "transitive invariance for self supervised visual representation learning"
    - "representation learning by learning to count"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "learning representations for automatic colorization"
    - "adversarial feature learning"

Connected through hyperedge HE-4 (induced by source paper "learning fashion compatibility across apparel categories for outfit recommendation"):
  with members:
    - "video2shop exactly matching clothes in videos to online shopping images"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "deep zero shot learning for scene sketch"):
  with members:
    - "learning deep sketch abstraction"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "a zero shot framework for sketch based image retrieval"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "generalized zero shot recognition based on visually semantic embedding"
    - "sketchyscene richly annotated scene sketches"
    - "unsupervised person re identification by deep learning tracklet association"
    - "semantic autoencoder for zero shot learning"

Connected through hyperedge HE-6 (induced by source paper "cloth3d clothed 3d humans"):
  with members:
    - "dressing 3d humans using a conditional mesh vae gan"
    - "mesh variational autoencoders with edge contraction pooling"
    - "synthetic data for deep learning"
    - "multi garment net learning to dress 3d people from images"
    - "simulcap single view human performance capture with cloth simulation"
    - "a comprehensive survey on graph neural networks"
    - "detailed human avatars from monocular video"
    - "garnet a two stream network for fast and accurate 3d cloth draping"

Connected through hyperedge HE-7 (induced by source paper "progressive fashion attribute extraction"):
  with members:
    - "a unified model with structured output for fashion images classification"
    - "progressive growing of gans for improved quality stability and variation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "deep imbalanced attribute classification using visual attention aggregation"):
  with members:
    - "deep visual attention prediction"
    - "harmonious attention network for person re identification"
    - "attend and rectify a gated attention mechanism for fine grained recovery"
    - "deep imbalanced learning for face recognition and attribute prediction"
    - "adaptive svm learning with privileged information for domain adaptation"
    - "focal loss for dense object detection"
    - "curriculum learning for multi task classification of visual attributes"
    - "order free rnn with visual attention for multi label classification"

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)