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 collaboration joint unsupervised learning of depth camera motion optical flow and motion segmentation"
  Abstract: We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions. Our key insight is that these four fundamental vision problems are coupled through geometric constraints. Consequently, learning to solve them together simplifies the problem because the solutions can reinforce each other. We go beyond previous work by exploiting geometry more explicitly and segmenting the scene into static and moving regions. To that end, we introduce Competitive Collaboration, a framework that facilitates the coordinated training of multiple specialized neural networks to solve complex problems. Competitive Collaboration works much like expectation-maximization, but with neural networks that act as both competitors to explain pixels that correspond to static or moving regions, and as collaborators through a moderator that assigns pixels to be either static or independently moving. Our novel method integrates all these problems in a common framework and simultaneously reasons about the segmentation of the scene into moving objects and the static background, the camera motion, depth of the static scene structure, and the optical flow of moving objects. Our model is trained without any supervision and achieves state-of-the-art performance among joint unsupervised methods on all sub-problems.

Connected through hyperedge HE-1 (induced by source paper "attentive single tasking of multiple tasks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "taskonomy disentangling task transfer learning"
    - "unified perceptual parsing for scene understanding"
    - "efficient video object segmentation via network modulation"
    - "multi task learning as multi objective optimization"

Connected through hyperedge HE-2 (induced by source paper "learning independent object motion from unlabelled stereoscopic videos"):
  with members:
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "fast multi frame stereo scene flow with motion segmentation"
    - "learning a multi view stereo machine"
    - "unsupervised learning of depth and ego motion from video"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"

Connected through hyperedge HE-3 (induced by source paper "bridging stereo matching and optical flow via spatiotemporal correspondence"):
  with members:
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "joint unsupervised learning of optical flow and depth by watching stereo videos"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "single view stereo matching"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "pyramid stereo matching network"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"

Connected through hyperedge HE-4 (induced by source paper "unsupervised video depth estimation based on ego motion and disparity consensus"):
  with members:
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "unsupervised depth estimation 3d face rotation and replacement"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "unsupervised learning of depth and ego motion from video"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"
    - "unsupervised monocular depth estimation with left right consistency"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"

Connected through hyperedge HE-5 (induced by source paper "unsupervised deep epipolar flow for stationary or dynamic scenes"):
  with members:
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "optical flow in mostly rigid scenes"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"
    - "robust multi body feature tracker a segmentation free approach"

Connected through hyperedge HE-6 (induced by source paper "digging into self supervised monocular depth estimation"):
  with members:
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "deep attention based classification network for robust depth prediction"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"

Connected through hyperedge HE-7 (induced by source paper "every pixel counts joint learning of geometry and motion with 3d holistic understanding"):
  with members:
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "lego learning edge with geometry all at once by watching videos"
    - "digging into self supervised monocular depth estimation"
    - "depth prediction without the sensors leveraging structure for unsupervised learning from monocular videos"

Connected through hyperedge HE-8 (induced by source paper "df net unsupervised joint learning of depth and flow using cross task consistency"):
  with members:
    - "learning single image depth from videos using quality assessment networks"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deepmvs learning multi view stereopsis"
    - "taskonomy disentangling task transfer learning"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "megadepth learning single view depth prediction from internet photos"
    - "lego learning edge with geometry all at once by watching videos"

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)