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: "panda pose aligned networks for deep attribute modeling"
  Abstract: We propose a method for inferring human attributes (such as gender, hair style, clothes style, expression, action) from images of people under large variation of viewpoint, pose, appearance, articulation and occlusion. Convolutional Neural Nets (CNN) have been shown to perform very well on large scale object recognition problems. In the context of attribute classification, however, the signal is often subtle and it may cover only a small part of the image, while the image is dominated by the effects of pose and viewpoint. Discounting for pose variation would require training on very large labeled datasets which are not presently available. Part-based models, such as poselets and DPM have been shown to perform well for this problem but they are limited by shallow low-level features. We propose a new method which combines part-based models and deep learning by training pose-normalized CNNs. We show substantial improvement vs. state-of-the-art methods on challenging attribute classification tasks in unconstrained settings. Experiments confirm that our method outperforms both the best part-based methods on this problem and conventional CNNs trained on the full bounding box of the person.

Connected through hyperedge HE-1 (induced by source paper "learning preference based similarities from face images using siamese multi task cnns"):
  with members:
    - "scut fbp5500 a diverse benchmark dataset for multi paradigm facial beauty prediction"
    - "smoothgrad removing noise by adding noise"
    - "an overview of multi task learning in deep neural networks"
    - "afif4 deep gender classification based on adaboost based fusion of isolated facial features and foggy faces"
    - "faceboxes a cpu real time face detector with high accuracy"
    - "cross stitch networks for multi task learning"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "densely connected convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "boosting deep face recognition via disentangling appearance and geometry"):
  with members:
    - "robust facial landmark detection via aggregation on geometrically manipulated faces"
    - "deformable generator network unsupervised disentanglement of appearance and geometry"
    - "fast geometrically perturbed adversarial faces"
    - "a deep face identification network enhanced by facial attributes prediction"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "improving facial attribute prediction using semantic segmentation"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"

Connected through hyperedge HE-3 (induced by source paper "spatial scale aligned network for fine grained recognition"):
  with members:
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "maximum entropy fine grained classification"
    - "learning to navigate for fine grained classification"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "feature pyramid networks for object detection"
    - "learning a discriminative filter bank within a cnn for fine grained recognition"
    - "discriminative scale space tracking"
    - "compact bilinear pooling"

Connected through hyperedge HE-4 (induced by source paper "deep multi task multi label cnn for effective facial attribute classification"):
  with members:
    - "multi task learning of cascaded cnn for facial attribute classification"
    - "end to end multi task learning with attention"
    - "segment based methods for facial attribute detection from partial faces"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "heterogeneous face attribute estimation a deep multi task learning approach"
    - "attributes for improved attributes a multi task network for attribute classification"
    - "leveraging mid level deep representations for predicting face attributes in the wild"
    - "moon a mixed objective optimization network for the recognition of facial attributes"

Connected through hyperedge HE-5 (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-6 (induced by source paper "facial synthesis from visual attributes via sketch using multi scale generators"):
  with members:
    - "polarimetric thermal to visible face verification via attribute preserved synthesis"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "face synthesis from visual attributes via sketch using conditional vaes and gans"
    - "on hallucinating context and background pixels from a face mask using multi scale gans"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "modulating early visual processing by language"
    - "decomposing motion and content for natural video sequence prediction"

Connected through hyperedge HE-7 (induced by source paper "on symbiosis of attribute prediction and semantic segmentation"):
  with members:
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "improving facial attribute prediction using semantic segmentation"
    - "class rectification hard mining for imbalanced deep learning"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "deep view sensitive pedestrian attribute inference in an end to end model"
    - "rethinking atrous convolution for semantic image segmentation"
    - "semantic object parsing with graph lstm"

Connected through hyperedge HE-8 (induced by source paper "improving person re identification by attribute and identity learning"):
  with members:
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "attention aware compositional network for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "person search with natural language description"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "adversarial attribute image person re identification"

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)