human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "joint unsupervised learning of optical flow and egomotion with bi level optimization"):
  Abstract: We address the problem of joint optical flow and camera motion estimation in rigid scenes by incorporating geometric constraints into an unsupervised deep learning framework. Unlike existing approaches which rely on brightness constancy and local smoothness for optical flow estimation, we exploit the global relationship between optical flow and camera motion using epipolar geometry. In particular, we formulate the prediction of optical flow and camera motion as a bi-level optimization problem, consisting of an upper-level problem to estimate the flow that conforms to the predicted camera motion, and a lower-level problem to estimate the camera motion given the predicted optical flow. We use implicit differentiation to enable back-propagation through the lower-level geometric optimization layer independent of its implementation, allowing end-to-end training of the network. With globally-enforced geometric constraints, we are able to improve the quality of the estimated optical flow in challenging scenarios and obtain better camera motion estimates compared to other unsupervised learning methods.

Includes members:
  Member "meta learning with differentiable convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cross domain few shot classification via learned feature wise transformation")
  Member "unsupervised deep epipolar flow for stationary or dynamic scenes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "occlusion aware unsupervised learning of optical flow from video")
  Member "unsupervised scale consistent depth and ego motion learning from monocular video":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "depth from videos in the wild unsupervised monocular depth learning from unknown cameras":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning depth via interaction")
  Member "deep declarative networks a new hope":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "differentiable convex optimization layers")
  Member "meta learning with implicit gradients":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers")
  Member "ddflow learning optical flow with unlabeled data distillation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scopeflow dynamic scene scoping for optical flow")
  Member "selflow self supervised learning of optical flow":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "foal fast online adaptive learning for cardiac motion estimation")

Question: Please tell me which class the source paper 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)