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: "matching cnn meets knn quasi parametric human parsing"
  Abstract: Both parametric and non-parametric approaches have demonstrated encouraging performances in the human parsing task, namely segmenting a human image into several semantic regions (e.g., hat, bag, left arm, face). In this work, we aim to develop a new solution with the advantages of both methodologies, namely supervision from annotated data and the flexibility to use newly annotated (possibly uncommon) images, and present a quasi-parametric human parsing model. Under the classic K Nearest Neighbor (KNN)-based nonparametric framework, the parametric Matching Convolutional Neural Network (M-CNN) is proposed to predict the matching confidence and displacements of the best matched region in the testing image for a particular semantic region in one KNN image. Given a testing image, we first retrieve its KNN images from the annotated/manually-parsed human image corpus. Then each semantic region in each KNN image is matched with confidence to the testing image using M-CNN, and the matched regions from all KNN images are further fused, followed by a superpixel smoothing procedure to obtain the ultimate human parsing result. The M-CNN differs from the classic CNN in that the tailored cross image matching filters are introduced to characterize the matching between the testing image and the semantic region of a KNN image. The cross image matching filters are defined at different convolutional layers, each aiming to capture a particular range of displacements. Comprehensive evaluations over a large dataset with 7,700 annotated human images well demonstrate the significant performance gain from the quasi-parametric model over the state-of-the-arts, for the human parsing task.

Connected through hyperedge HE-1 (induced by source paper "hierarchical human parsing with typed part relation reasoning"):
  with members:
    - "learning compositional neural information fusion for human parsing"
    - "zero shot video object segmentation via attentive graph neural networks"
    - "graspnet a large scale clustered and densely annotated datase for object grasping"
    - "reasoning visual dialogs with structural and partial observations"
    - "understanding human gaze communication by spatio temporal graph reasoning"
    - "graphonomy universal human parsing via graph transfer learning"
    - "spgnet semantic prediction guidance for scene parsing"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"

Connected through hyperedge HE-2 (induced by source paper "learning compositional neural information fusion for human parsing"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "instance level human parsing via part grouping network"
    - "crowdpose efficient crowded scenes pose estimation and a new benchmark"
    - "relational inductive biases deep learning and graph networks"
    - "learning human object interactions by graph parsing neural networks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "macro micro adversarial network for human parsing"
    - "holistic instance level human parsing"

Connected through hyperedge HE-3 (induced by source paper "deep portrait image completion and extrapolation"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "everybody dance now"
    - "generative image inpainting with contextual attention"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "generative face completion"
    - "a generative model of people in clothing"

Connected through hyperedge HE-4 (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-5 (induced by source paper "generalized feedback loop for joint hand object pose estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "hand3d hand pose estimation using 3d neural network"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "dynamics based 3d skeletal hand tracking"

Connected through hyperedge HE-6 (induced by source paper "pose guided fashion image synthesis using deep generative model"):
  with members:
    - "a variational u net for conditional appearance and shape generation"
    - "everybody dance now"
    - "viton an image based virtual try on network"
    - "pose guided person image generation"
    - "looking at outfit to parse clothing"
    - "a generative model of people in clothing"
    - "be your own prada fashion synthesis with structural coherence"
    - "visual search at ebay"

Connected through hyperedge HE-7 (induced by source paper "adaptive temporal encoding network for video instance level human parsing"):
  with members:
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "visual question reasoning on general dependency tree"
    - "dual motion gan for future flow embedded video prediction"
    - "holistic instance level human parsing"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "flow guided feature aggregation for video object detection"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"

Connected through hyperedge HE-8 (induced by source paper "instance level human parsing via part grouping network"):
  with members:
    - "holistic instance level human parsing"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "richer convolutional features for edge detection"
    - "interpretable structure evolving lstm"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"
    - "semantic object parsing with graph lstm"

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)