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: "object category understanding via eye fixations on freehand sketches"
  Abstract: The study of eye gaze fixations on photographic images is an active research area. In contrast, the image sub-category of freehand sketches has not received as much attention for such studies. In this paper, we analyze the results of a free-viewing gaze fixation study conducted on 3904 freehand sketches distributed across 160 object categories. Our analysis shows that fixation sequences exhibit marked consistency within a sketch, across sketches of a category and even across suitably grouped sets of categories. This multi-level consistency is remarkable given the variability in depiction and extreme image content sparsity that characterizes hand-drawn object sketches. In this paper, we show that the multi-level consistency in the fixation data can be exploited to 1) predict a test sketch’s category given only its fixation sequence and 2) build a computational model which predicts part-labels underlying fixations on objects. We hope that our findings motivate the community to deem sketch-like representations worthy of gaze-based studies vis-a-vis photographic images.

Connected through hyperedge HE-1 (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"
    - "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"
    - "enabling my robot to play pictionary recurrent neural networks for sketch recognition"

Connected through hyperedge HE-2 (induced by source paper "sketch specific data augmentation for freehand sketch recognition"):
  with members:
    - "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"
    - "multi view convolutional neural networks for 3d shape recognition"

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)