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: "enabling my robot to play pictionary recurrent neural networks for sketch recognition"
  Abstract: Freehand sketching is an inherently sequential process. Yet, most approaches for hand-drawn sketch recognition either ignore this sequential aspect or exploit it in an ad-hoc manner. In our work, we propose a recurrent neural network architecture for sketch object recognition which exploits the long-term sequential and structural regularities in stroke data in a scalable manner. Specifically, we introduce a Gated Recurrent Unit based framework which leverages deep sketch features and weighted per-timestep loss to achieve state-of-the-art results on a large database of freehand object sketches across a large number of object categories. The inherently online nature of our framework is especially suited for on-the-fly recognition of objects as they are being drawn. Thus, our framework can enable interesting applications such as camera-equipped robots playing the popular party game Pictionary with human players and generating sparsified yet recognizable sketches of objects.

Connected through hyperedge HE-1 (induced by source paper "multi graph transformer for free hand sketch recognition"):
  with members:
    - "semantically tied paired cycle consistency for zero shot sketch based image retrieval"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "learning deep transformer models for machine translation"
    - "livesketch query perturbations for guided sketch based visual search"
    - "transformer xl attentive language models beyond a fixed length context"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "bp transformer modelling long range context via binary partitioning"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-2 (induced by source paper "teaching gans to sketch in vector format"):
  with members:
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "sketch pix2seq a model to generate sketches of multiple categories"
    - "object category understanding via eye fixations on freehand sketches"
    - "image generation from sketch constraint using contextual gan"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "sketchparse towards rich descriptions for poorly drawn sketches using multi task hierarchical deep networks"
    - "a neural representation of sketch drawings"
    - "high resolution image synthesis and semantic manipulation with conditional gans"

Connected through hyperedge HE-3 (induced by source paper "sketch specific data augmentation for freehand sketch recognition"):
  with members:
    - "object category understanding via eye fixations on freehand sketches"
    - "sketchparse towards rich descriptions for poorly drawn sketches using multi task hierarchical deep networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "scribbler controlling deep image synthesis with sketch and color"
    - "sketch based 3d shape retrieval using convolutional neural networks"
    - "accurate image super resolution using very deep convolutional networks"
    - "multi view convolutional neural networks for 3d shape recognition"

Connected through hyperedge HE-4 (induced by source paper "sketchparse towards rich descriptions for poorly drawn sketches using multi task hierarchical deep networks"):
  with members:
    - "segicp integrated deep semantic segmentation and pose estimation"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "semantic segmentation using adversarial networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "improved deep learning of object category using pose information"
    - "network of experts for large scale image categorization"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "stackgan text to photo realistic image synthesis with stacked generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "cnn fixations an unraveling approach to visualize the discriminative image regions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "fast feature fool a data independent approach to universal adversarial perturbations"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "training region based object detectors with online hard example mining"
    - "densely connected convolutional networks"
    - "a powerful generative model using random weights for the deep image representation"
    - "top down neural attention by excitation backprop"

Connected through hyperedge HE-6 (induced by source paper "deepfuse a deep unsupervised approach for exposure fusion with extreme exposure image pairs"):
  with members:
    - "image denoising via cnns an adversarial approach"
    - "mask r cnn"
    - "r fcn object detection via region based fully convolutional networks"
    - "vqa visual question answering"
    - "deep residual learning for image recognition"
    - "loss functions for neural networks for image processing"
    - "recurrent convolutional neural networks for scene parsing"

Connected through hyperedge HE-7 (induced by source paper "sequential dual deep learning with shape and texture features for sketch recognition"):
  with members:
    - "sketch based 3d shape retrieval using convolutional neural networks"
    - "gated feedback recurrent neural networks"
    - "sketch a net that beats humans"
    - "very deep convolutional networks for large scale image recognition"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)