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: "bottom up and top down reasoning with hierarchical rectified gaussians"
  Abstract: Convolutional neural nets (CNNs) have demonstrated remarkable performance in recent history. Such approaches tend to work in a unidirectional bottom-up feed-forward fashion. However, practical experience and biological evidence tells us that feedback plays a crucial role, particularly for detailed spatial understanding tasks. This work explores bidirectional architectures that also reason with top-down feedback: neural units are influenced by both lower and higher-level units. #R##N#We do so by treating units as rectified latent variables in a quadratic energy function, which can be seen as a hierarchical Rectified Gaussian model (RGs). We show that RGs can be optimized with a quadratic program (QP), that can in turn be optimized with a recurrent neural network (with rectified linear units). This allows RGs to be trained with GPU-optimized gradient descent. From a theoretical perspective, RGs help establish a connection between CNNs and hierarchical probabilistic models. From a practical perspective, RGs are well suited for detailed spatial tasks that can benefit from top-down reasoning. We illustrate them on the challenging task of keypoint localization under occlusions, where local bottom-up evidence may be misleading. We demonstrate state-of-the-art results on challenging benchmarks.

Connected through hyperedge HE-1 (induced by source paper "deep high resolution representation learning for human pose estimation"):
  with members:
    - "quantized densely connected u nets for efficient landmark localization"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "pose flow efficient online pose tracking"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "multi scale structure aware network for human pose estimation"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "multiposenet fast multi person pose estimation using pose residual network"

Connected through hyperedge HE-2 (induced by source paper "human pose estimation with spatial contextual information"):
  with members:
    - "path aggregation network for instance segmentation"
    - "situation recognition with graph neural networks"
    - "learning feature pyramids for human pose estimation"
    - "self adversarial training for human pose estimation"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "multi context attention for human pose estimation"
    - "mask r cnn"
    - "convolutional pose machines"

Connected through hyperedge HE-3 (induced by source paper "cascade feature aggregation for human pose estimation"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "human pose estimation with spatial contextual information"
    - "quantized densely connected u nets for efficient landmark localization"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "multi scale structure aware network for human pose estimation"
    - "learning to refine human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "learning feature pyramids for human pose estimation"

Connected through hyperedge HE-4 (induced by source paper "anchor loss modulating loss scale based on prediction difficulty"):
  with members:
    - "multi scale structure aware network for human pose estimation"
    - "smooth loss functions for deep top k classification"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "benchmarking and error diagnosis in multi instance pose estimation"
    - "focal loss for dense object detection"

Connected through hyperedge HE-5 (induced by source paper "quantized densely connected u nets for efficient landmark localization"):
  with members:
    - "learning to forecast and refine residual motion for image to video generation"
    - "red net a recurrent encoder decoder network for video based face alignment"
    - "training and inference with integers in deep neural networks"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "learning feature pyramids for human pose estimation"
    - "memory efficient implementation of densenets"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"

Connected through hyperedge HE-6 (induced by source paper "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"):
  with members:
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "learning feature pyramids for human pose estimation"
    - "self adversarial training for human pose estimation"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "image de raining using a conditional generative adversarial network"
    - "multi context attention for human pose estimation"

Connected through hyperedge HE-7 (induced by source paper "learning to detect and track visible and occluded body joints in a virtual world"):
  with members:
    - "simple online and realtime tracking with a deep association metric"
    - "detect to track and track to detect"
    - "posetrack a benchmark for human pose estimation and tracking"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "online multi object tracking using cnn based single object tracker with spatial temporal attention mechanism"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "posetrack joint multi person pose estimation and tracking"

Connected through hyperedge HE-8 (induced by source paper "deep component analysis via alternating direction neural networks"):
  with members:
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "unrolled optimization with deep priors"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "understanding deep learning requires rethinking generalization"
    - "identity mappings in deep residual networks"
    - "speed accuracy trade offs for modern convolutional object detectors"

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)