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: "parallel multi dimensional lstm with application to fast biomedical volumetric image segmentation"
  Abstract: Convolutional Neural Networks (CNNs) can be shifted across 2D images or 3D videos to segment them. They have a fixed input size and typically perceive only small local contexts of the pixels to be classified as foreground or background. In contrast, Multi-Dimensional Recurrent NNs (MD-RNNs) can perceive the entire spatio-temporal context of each pixel in a few sweeps through all pixels, especially when the RNN is a Long Short-Term Memory (LSTM). Despite these theoretical advantages, however, unlike CNNs, previous MD-LSTM variants were hard to parallelize on GPUs. Here we re-arrange the traditional cuboid order of computations in MD-LSTM in pyramidal fashion. The resulting PyraMiD-LSTM is easy to parallelize, especially for 3D data such as stacks of brain slice images. PyraMiD-LSTM achieved best known pixel-wise brain image segmentation results on MRBrainS13 (and competitive results on EM-ISBI12).

Connected through hyperedge HE-1 (induced by source paper "secure and robust machine learning for healthcare a survey"):
  with members:
    - "securing future autonomous connected vehicles challenges posed by adversarial machine learning and the way forward"
    - "reproducibility in machine learning for health"
    - "fairness in algorithmic decision making an excursion through the lens of causality"
    - "caveat emptor the risks of using big data for human development"
    - "motion corrected multishot mri reconstruction using generative networks with sensitivity encoding"
    - "the adversarial machine learning conundrum can the insecurity of ml become the achilles heel of cognitive networks"
    - "self supervised domain adaptation for computer vision tasks"
    - "feature robustness in non stationary health records caveats to deployable model performance in common clinical machine learning tasks"

Connected through hyperedge HE-2 (induced by source paper "arma nets expanding receptive field for dense prediction"):
  with members:
    - "scale aware trident networks for object detection"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "deformable convnets v2 more deformable better results"
    - "smoothed dilated convolutions for improved dense prediction"
    - "contextvp fully context aware video prediction"
    - "active convolution learning the shape of convolution for image classification"
    - "learning affinity via spatial propagation networks"
    - "deformable convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "deep convolutional neural networks for brain image analysis on magnetic resonance imaging a review"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "a survey on deep learning in medical image analysis"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "generalised wasserstein dice score for imbalanced multi class segmentation using holistic convolutional networks"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"

Connected through hyperedge HE-4 (induced by source paper "veritatem dies aperit temporally consistent depth prediction enabled by a multi task geometric and semantic scene understanding approach"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "learning a discriminative feature network for semantic segmentation"
    - "deep depth completion of a single rgb d image"
    - "dynamic video segmentation network"
    - "low latency video semantic segmentation"
    - "generative image inpainting with contextual attention"
    - "skip connections eliminate singularities"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "a review of modularization techniques in artificial neural networks"):
  with members:
    - "knowledge transfer in deep block modular neural networks"
    - "graph hypernetworks for neural architecture search"
    - "mattnet modular attention network for referring expression comprehension"
    - "relational inductive biases deep learning and graph networks"
    - "a graph to sequence model for amr to text generation"
    - "dense and diverse capsule networks making the capsules learn better"
    - "evolving deep neural networks"
    - "hierarchical semantic segmentation using modular convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "cubic lstms for video prediction"):
  with members:
    - "self supervised visual planning with temporal skip connections"
    - "decomposing motion and content for natural video sequence prediction"
    - "motion prediction under multimodality with conditional stochastic networks"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"
    - "recurrent environment simulators"
    - "mocogan decomposing motion and content for video generation"
    - "learning to generate time lapse videos using multi stage dynamic generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "medical image super resolution using a generative adversarial network"):
  with members:
    - "a multi channel network with image retrieval for accurate brain tissue segmentation"
    - "a strategy of mr brain tissue images suggestive annotation based on modified u net"
    - "perceptual losses for real time style transfer and super resolution"
    - "accelerating the super resolution convolutional neural network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "identity mappings in deep residual networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "automatic segmentation of mr brain images with a convolutional neural network"

Connected through hyperedge HE-8 (induced by source paper "elsa a throughput optimized design of an lstm accelerator for energy constrained devices"):
  with members:
    - "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"
    - "c lstm enabling efficient lstm using structured compression techniques on fpgas"
    - "in datacenter performance analysis of a tensor processing unit"
    - "ese efficient speech recognition engine with sparse lstm on fpga"
    - "fpga based low power speech recognition with recurrent neural networks"
    - "single stream parallelization of generalized lstm like rnns on a gpu"
    - "unsupervised learning of video representations using lstms"
    - "deep speech 2 end to end speech recognition in english and mandarin"

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)