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: "deep active contours"
  Abstract: We propose a method for interactive boundary extraction which combines a deep, patch-based representation with an active contour framework. We train a class-specific convolutional neural network which predicts a vector pointing from the respective point on the evolving contour towards the closest point on the boundary of the object of interest. These predictions form a vector field which is then used for evolving the contour by the Sobolev active contour framework proposed by Sundaramoorthi et al. The resulting interactive segmentation method is very efficient in terms of required computational resources and can even be trained on comparatively small graphics cards. We evaluate the potential of the proposed method on both medical and non-medical challenge data sets, such as the STACOM data set and the PASCAL VOC 2012 data set.

Connected through hyperedge HE-1 (induced by source paper "deep snake for real time instance segmentation"):
  with members:
    - "dense reppoints representing visual objects with dense point sets"
    - "mask scoring r cnn"
    - "ssap single shot instance segmentation with affinity pyramid"
    - "objects as points"
    - "fast interactive object annotation with curve gcn"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "bottom up object detection by grouping extreme and center points"
    - "explicit shape encoding for real time instance segmentation"

Connected through hyperedge HE-2 (induced by source paper "deep learning in cardiology"):
  with members:
    - "pixelbnn augmenting the pixelcnn with batch normalization and the presentation of a fast architecture for retinal vessel segmentation"
    - "estimation of the volume of the left ventricle from mri images using deep neural networks"
    - "deep learning analysis of the myocardium in coronary ct angiography for identification of patients with functionally significant coronary artery stenosis"
    - "convolutional capsule network for classification of breast cancer histology images"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "brain tumor type classification via capsule networks"
    - "deepheart semi supervised sequence learning for cardiovascular risk prediction"

Connected through hyperedge HE-3 (induced by source paper "end to end trainable active contours via differentiable rendering"):
  with members:
    - "cnn based semantic segmentation using level set loss"
    - "darnet deep active ray network for building segmentation"
    - "mesh r cnn"
    - "fast interactive object annotation with curve gcn"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "improved breast mass segmentation in mammograms with conditional residual u net"
    - "breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network"
    - "unsupervised learning of shape and pose with differentiable point clouds"

Connected through hyperedge HE-4 (induced by source paper "darnet deep active ray network for building segmentation"):
  with members:
    - "fast interactive object annotation with curve gcn"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "path aggregation network for instance segmentation"
    - "dilated residual networks"
    - "annotating object instances with a polygon rnn"
    - "mask r cnn"
    - "deep watershed transform for instance segmentation"
    - "torontocity seeing the world with a million eyes"

Connected through hyperedge HE-5 (induced by source paper "devil is in the edges learning semantic boundaries from noisy annotations"):
  with members:
    - "vlase vehicle localization by aggregating semantic edges"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "simultaneous edge alignment and learning"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deep watershed transform for instance segmentation"
    - "image to image translation with conditional adversarial networks"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-6 (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-7 (induced by source paper "learning deep structured active contours end to end"):
  with members:
    - "learning aerial image segmentation from online maps"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "annotating object instances with a polygon rnn"
    - "semantic instance segmentation via deep metric learning"
    - "deep watershed transform for instance segmentation"
    - "torontocity seeing the world with a million eyes"
    - "r fcn object detection via region based fully convolutional networks"
    - "instance aware semantic segmentation via multi task network cascades"

Connected through hyperedge HE-8 (induced by source paper "end to end learning of convolutional neural net and dynamic programming for left ventricle segmentation"):
  with members:
    - "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation"
    - "decoupled neural interfaces using synthetic gradients"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

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)