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: "reconstructive sparse code transfer for contour detection and semantic labeling"
  Abstract: We frame the task of predicting a semantic labeling as a sparse reconstruction procedure that applies a target-specific learned transfer function to a generic deep sparse code representation of an image. This strategy partitions training into two distinct stages. First, in an unsupervised manner, we learn a set of generic dictionaries optimized for sparse coding of image patches. We train a multilayer representation via recursive sparse dictionary learning on pooled codes output by earlier layers. Second, we encode all training images with the generic dictionaries and learn a transfer function that optimizes reconstruction of patches extracted from annotated ground-truth given the sparse codes of their corresponding image patches. At test time, we encode a novel image using the generic dictionaries and then reconstruct using the transfer function. The output reconstruction is a semantic labeling of the test image. #R##N#Applying this strategy to the task of contour detection, we demonstrate performance competitive with state-of-the-art systems. Unlike almost all prior work, our approach obviates the need for any form of hand-designed features or filters. To illustrate general applicability, we also show initial results on semantic part labeling of human faces. #R##N#The effectiveness of our approach opens new avenues for research on deep sparse representations. Our classifiers utilize this representation in a novel manner. Rather than acting on nodes in the deepest layer, they attach to nodes along a slice through multiple layers of the network in order to make predictions about local patches. Our flexible combination of a generatively learned sparse representation with discriminatively trained transfer classifiers extends the notion of sparse reconstruction to encompass arbitrary semantic labeling tasks.

Connected through hyperedge HE-1 (induced by source paper "simultaneous edge alignment and learning"):
  with members:
    - "boundary cues for 3d object shape recovery"
    - "iterative learning with open set noisy labels"
    - "casenet deep category aware semantic edge detection"
    - "richer convolutional features for edge detection"
    - "annotating object instances with a polygon rnn"
    - "toward robustness against label noise in training deep discriminative neural networks"
    - "learning from noisy large scale datasets with minimal supervision"
    - "learning from noisy labels with distillation"

Connected through hyperedge HE-2 (induced by source paper "discovery of visual semantics by unsupervised and self supervised representation learning"):
  with members:
    - "colorization as a proxy task for visual understanding"
    - "harmonic networks deep translation and rotation equivariance"
    - "learning representations for automatic colorization"
    - "deep multi task representation learning a tensor factorisation approach"
    - "ambient sound provides supervision for visual learning"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "cross stitch networks for multi task learning"
    - "high performance semantic segmentation using very deep fully convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "colorization as a proxy task for visual understanding"):
  with members:
    - "learning representations for automatic colorization"
    - "ambient sound provides supervision for visual learning"
    - "unsupervised learning using sequential verification for action recognition"
    - "high performance semantic segmentation using very deep fully convolutional networks"
    - "adversarial feature learning"
    - "learning features by watching objects move"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "split brain autoencoders unsupervised learning by cross channel prediction"

Connected through hyperedge HE-4 (induced by source paper "casenet deep category aware semantic edge detection"):
  with members:
    - "boundary cues for 3d object shape recovery"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "convolutional oriented boundaries"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "holistically nested edge detection"
    - "weakly supervised object boundaries"
    - "pixel wise deep learning for contour detection"
    - "high for low and low for high efficient boundary detection from deep object features and its applications to high level vision"

Connected through hyperedge HE-5 (induced by source paper "contour detection from deep patch level boundary prediction"):
  with members:
    - "n 4 fields neural network nearest neighbor fields for image transforms"
    - "fast edge detection using structured forests"
    - "generalized boundaries from multiple image interpretations"

Connected through hyperedge HE-6 (induced by source paper "fractalnet ultra deep neural networks without residuals"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "resnet in resnet generalizing residual architectures"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "wide residual networks"
    - "all you need is a good init"

Connected through hyperedge HE-7 (induced by source paper "learning representations for automatic colorization"):
  with members:
    - "adversarial feature learning"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "context encoders feature learning by inpainting"
    - "colorful image colorization"
    - "unsupervised visual representation learning by context prediction"
    - "holistically nested edge detection"
    - "parsenet looking wider to see better"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "ilgnet inception modules with connected local and global features for efficient image aesthetic quality classification using domain adaptation"):
  with members:
    - "embedding structured contour and location prior in siamesed fully convolutional networks for road detection"
    - "a joint convolutional neural networks and context transfer for street scenes labeling"
    - "deep aesthetic quality assessment with semantic information"
    - "a lamp adaptive layout aware multi patch deep convolutional neural network for photo aesthetic assessment"
    - "visual aesthetic quality assessment with multi task deep learning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "photo aesthetics ranking network with attributes and content adaptation"
    - "deep residual learning for image 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)