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: "sketch a net that beats humans"
  Abstract: We propose a multi-scale multi-channel deep neural network framework that, for the first time, yields sketch recognition performance surpassing that of humans. Our superior performance is a result of explicitly embedding the unique characteristics of sketches in our model: (i) a network architecture designed for sketch rather than natural photo statistics, (ii) a multi-channel generalisation that encodes sequential ordering in the sketching process, and (iii) a multi-scale network ensemble with joint Bayesian fusion that accounts for the different levels of abstraction exhibited in free-hand sketches. We show that state-of-the-art deep networks specifically engineered for photos of natural objects fail to perform well on sketch recognition, regardless whether they are trained using photo or sketch. Our network on the other hand not only delivers the best performance on the largest human sketch dataset to date, but also is small in size making efficient training possible using just CPUs.

Connected through hyperedge HE-1 (induced by source paper "learning robust representations via multi view information bottleneck"):
  with members:
    - "semantically tied paired cycle consistency for zero shot sketch based image retrieval"
    - "on mutual information maximization for representation learning"
    - "contrastive multiview coding"
    - "on variational bounds of mutual information"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mine mutual information neural estimation"
    - "representation learning with contrastive predictive coding"
    - "deep variational canonical correlation analysis"

Connected through hyperedge HE-2 (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-3 (induced by source paper "semantically tied paired cycle consistency for zero shot sketch based image retrieval"):
  with members:
    - "deep cross modality adaptation via semantics preserving adversarial learning for sketch based 3d shape retrieval"
    - "zero shot sketch image hashing"
    - "a zero shot framework for sketch based image retrieval"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "semantic autoencoder for zero shot learning"
    - "zero shot learning via class conditioned deep generative models"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "deep sketch hashing fast free hand sketch based image retrieval"

Connected through hyperedge HE-4 (induced by source paper "goal driven sequential data abstraction"):
  with members:
    - "learning deep sketch abstraction"
    - "game of sketches deep recurrent models of pictionary style word guessing"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "dtr gan dilated temporal relational adversarial network for video summarization"
    - "video summarisation by classification with deep reinforcement learning"
    - "discovering visual concept structure with sparse and incomplete tags"
    - "sequential dual deep learning with shape and texture features for sketch recognition"
    - "neural extractive summarization with side information"

Connected through hyperedge HE-5 (induced by source paper "stacked semantic guided network for zero shot sketch based image retrieval"):
  with members:
    - "shared predictive cross modal deep quantization"
    - "triplet based deep hashing network for cross modal retrieval"
    - "zero shot sketch image hashing"
    - "a zero shot framework for sketch based image retrieval"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "semantic autoencoder for zero shot learning"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"

Connected through hyperedge HE-6 (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"
    - "enabling my robot to play pictionary recurrent neural networks for sketch recognition"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "an unpaired sketch to photo translation model"):
  with members:
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
    - "learning to sketch with shortcut cycle consistency"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "free form image inpainting with gated convolution"
    - "deep factorised inverse sketching"
    - "progressive growing of gans for improved quality stability and variation"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "a neural representation of sketch drawings"

Connected through hyperedge HE-8 (induced by source paper "sketchzooms deep multi view descriptors for matching line drawings"):
  with members:
    - "neural best buddies sparse cross domain correspondence"
    - "triplet center loss for multi view 3d object retrieval"
    - "sampling matters in deep embedding learning"
    - "3d shape reconstruction from sketches via multi view convolutional networks"
    - "mastering sketching adversarial augmentation for structured prediction"
    - "a neural representation of sketch drawings"
    - "3d sketching using multi view deep volumetric prediction"
    - "learning aligned cross modal representations from weakly aligned data"

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)