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: "adversarial structure matching loss for image segmentation"
  Abstract: The per-pixel cross-entropy loss (CEL) has been widely used in structured output prediction tasks as a spatial extension of generic image classification. However, its i.i.d. assumption neglects the structural regularity present in natural images. Various attempts have been made to incorporate structural reasoning mostly through structure priors in a cooperative way where co-occuring patterns are encouraged. We, on the other hand, approach this problem from an opposing angle and propose a new framework for training such structured prediction networks via an adversarial process, in which we train a structure analyzer that provides the supervisory signals, the adversarial structure matching loss (ASML). The structure analyzer is trained to maximize ASML, or to exaggerate recurring structural mistakes usually among co-occurring patterns. On the contrary, the structured output prediction network is trained to reduce those mistakes and is thus enabled to distinguish fine-grained structures. As a result, training structured output prediction networks using ASML reduces contextual confusion among objects and improves boundary localization. We demonstrate that ASML outperforms its counterpart CEL especially in context and boundary aspects on figure-ground segmentation and semantic segmentation tasks with various base architectures, such as FCN, U-Net, DeepLab, and PSPNet.

Connected through hyperedge HE-1 (induced by source paper "learning structure via consensus for face segmentation and parsing"):
  with members:
    - "fsgan subject agnostic face swapping and reenactment"
    - "does generative face completion help face recognition"
    - "face mask extraction in video sequence"
    - "learning discriminators as energy networks in adversarial learning"
    - "adaptive affinity fields for semantic segmentation"
    - "face completion with semantic knowledge and collaborative adversarial learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "adversarial learning for semi supervised semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "i bet you are wrong gambling adversarial networks for structured semantic segmentation"):
  with members:
    - "understanding deep learning techniques for image segmentation"
    - "star shape prior in fully convolutional networks for skin lesion segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "student beats the teacher deep neural networks for lateral ventricles segmentation in brain mr"
    - "efficient relaxations for dense crfs with sparse higher order potentials"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "adversarial learning for semi supervised semantic segmentation"
    - "el gan embedding loss driven generative adversarial networks for lane detection"

Connected through hyperedge HE-3 (induced by source paper "object segmentation using pixel wise adversarial loss"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "adversarial learning for semi supervised semantic segmentation"
    - "averaging weights leads to wider optima and better generalization"
    - "cycada cycle consistent adversarial domain adaptation"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "progressive growing of gans for improved quality stability and variation"
    - "large kernel matters improve semantic segmentation by global convolutional network"

Connected through hyperedge HE-4 (induced by source paper "el gan embedding loss driven generative adversarial networks for lane detection"):
  with members:
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "towards end to end lane detection an instance segmentation approach"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "splenomegaly segmentation using global convolutional kernels and conditional generative adversarial networks"
    - "adversarial training and dilated convolutions for brain mri segmentation"
    - "adversarial networks for the detection of aggressive prostate cancer"
    - "spatial as deep spatial cnn for traffic scene understanding"

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)