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: "image labeling by assignment"
  Abstract: We introduce a novel geometric approach to the image labeling problem. Abstracting from specific labeling applications, a general objective function is defined on a manifold of stochastic matrices, whose elements assign prior data that are given in any metric space, to observed image measurements. The corresponding Riemannian gradient flow entails a set of replicator equations, one for each data point, that are spatially coupled by geometric averaging on the manifold. Starting from uniform assignments at the barycenter as natural initialization, the flow terminates at some global maximum, each of which corresponds to an image labeling that uniquely assigns the prior data. Our geometric variational approach constitutes a smooth non-convex inner approximation of the general image labeling problem, implemented with sparse interior-point numerics in terms of parallel multiplicative updates that converge efficiently.

Connected through hyperedge HE-1 (induced by source paper "unsupervised assignment flow label learning on feature manifolds by spatially regularized geometric assignment"):
  with members:
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "sparse coding on symmetric positive definite manifolds using bregman divergences"

Connected through hyperedge HE-2 (induced by source paper "textually enriched neural module networks for visual question answering"):
  with members:
    - "dual attention networks for multimodal reasoning and matching"
    - "learning to compose neural networks for question answering"
    - "hierarchical question image co attention for visual question answering"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "ask me anything free form visual question answering based on knowledge from external sources"
    - "exploring models and data for image question answering"
    - "vqa visual question answering"

Connected through hyperedge HE-3 (induced by source paper "a transportation l p distance for signal analysis"):
  with members:
    - "transport based analysis modeling and learning from signal and data distributions"
    - "the radon cumulative distribution transform and its application to image classification"
    - "the cumulative distribution transform and linear pattern classification"
    - "learning with a wasserstein loss"
    - "imaging with kantorovich rubinstein discrepancy"

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)