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: "pix2code generating code from a graphical user interface screenshot"
  Abstract: Transforming a graphical user interface screenshot created by a designer into computer code is a typical task conducted by a developer in order to build customized software, websites, and mobile applications. In this paper, we show that deep learning methods can be leveraged to train a model end-to-end to automatically generate code from a single input image with over 77% of accuracy for three different platforms (i.e. iOS, Android and web-based technologies).

Connected through hyperedge HE-1 (induced by source paper "auto completion of user interface layout design using transformer based tree decoders"):
  with members:
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "tree to tree neural networks for program translation"
    - "syntax directed variational autoencoder for structured data"
    - "automatic graphics program generation using attention based hierarchical decoder"
    - "attention is all you need"
    - "a neural representation of sketch drawings"
    - "language to logical form with neural attention"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "guicomp a gui design assistant with real time multi faceted feedback"):
  with members:
    - "automated reporting of gui design violations for mobile apps"
    - "machine learning based prototyping of graphical user interfaces for mobile apps"
    - "a computational method for evaluating ui patterns"
    - "learning visual importance for graphic designs and data visualizations"

Connected through hyperedge HE-3 (induced by source paper "sketch2code generating a website from a paper mockup"):
  with members:
    - "machine learning based prototyping of graphical user interfaces for mobile apps"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "richer convolutional features for edge detection"
    - "deep direct regression for multi oriented scene text detection"
    - "r2cnn rotational region cnn for orientation robust scene text detection"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-4 (induced by source paper "cloudifiernet deep vision models for artificial image processing"):
  with members:
    - "focal loss for dense object detection"
    - "deep convolutional neural network inference with floating point weights and fixed point activations"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "efficient stochastic inference of bitwise deep neural networks"
    - "wide residual networks"
    - "xception deep learning with depthwise separable convolutions"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-5 (induced by source paper "learning to infer user interface attributes from images"):
  with members:
    - "3d interpreter networks for viewer centered wireframe modeling"
    - "image generation from scene graphs"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "learning to infer graphics programs from hand drawn images"
    - "learning from simulated and unsupervised images through adversarial training"
    - "synthetic data for text localisation in natural images"
    - "deep convolutional inverse graphics network"

Connected through hyperedge HE-6 (induced by source paper "sketch2code transformation of sketches to ui in real time using deep neural network"):
  with members:
    - "yolov3 an incremental improvement"
    - "focal loss for dense object detection"
    - "deep saliency what is learnt by a deep network about saliency"
    - "robust visual tracking via hierarchical convolutional features"
    - "deepcoder learning to write programs"
    - "feature pyramid networks for object detection"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "storydroid automated generation of storyboard for android apps"):
  with members:
    - "a large scale empirical study on industrial fake apps"
    - "understanding android obfuscation techniques a large scale investigation in the wild"
    - "ausera large scale automated security risk assessment of global mobile banking apps"
    - "efficiently manifesting asynchronous programming errors in android apps"
    - "automated reporting of gui design violations for mobile apps"
    - "machine learning based prototyping of graphical user interfaces for mobile apps"
    - "large scale analysis of framework specific exceptions in android apps"
    - "automated poisoning attacks and defenses in malware detection systems an adversarial machine learning approach"

Connected through hyperedge HE-8 (induced by source paper "data2vis automatic generation of data visualizations using sequence to sequence recurrent neural networks"):
  with members:
    - "beyond heuristics learning visualization design"
    - "vizml a machine learning approach to visualization recommendation"
    - "tree to tree neural networks for program translation"
    - "foresight recommending visual insights"
    - "progressive growing of gans for improved quality stability and variation"
    - "massive exploration of neural machine translation architectures"
    - "task based effectiveness of basic visualizations"
    - "a syntactic neural model for general purpose code generation"

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.LG(Machine Learning)