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: "optimal transport for domain adaptation"
  Abstract: Domain adaptation from one data space (or domain) to another is one of the most challenging tasks of modern data analytics. If the adaptation is done correctly, models built on a specific data space become more robust when confronted to data depicting the same semantic concepts (the classes), but observed by another observation system with its own specificities. Among the many strategies proposed to adapt a domain to another, finding a common representation has shown excellent properties: by finding a common representation for both domains, a single classifier can be effective in both and use labelled samples from the source domain to predict the unlabelled samples of the target domain. In this paper, we propose a regularized unsupervised optimal transportation model to perform the alignment of the representations in the source and target domains. We learn a transportation plan matching both PDFs, which constrains labelled samples in the source domain to remain close during transport. This way, we exploit at the same time the few labeled information in the source and the unlabelled distributions observed in both domains. Experiments in toy and challenging real visual adaptation examples show the interest of the method, that consistently outperforms state of the art approaches.

Connected through hyperedge HE-1 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "orthogonal deep neural networks"
    - "unsupervised domain adaptation via regularized conditional alignment"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "beyond sharing weights for deep domain adaptation"
    - "domain symmetric networks for adversarial domain adaptation"
    - "bridging theory and algorithm for domain adaptation"
    - "learning to transfer examples for partial domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "cross domain self supervised learning for domain adaptation with few source labels"):
  with members:
    - "universal domain adaptation through self supervision"
    - "drop to adapt learning discriminative features for unsupervised domain adaptation"
    - "unsupervised deep learning by neighbourhood discovery"
    - "confidence regularized self training"
    - "momentum contrast for unsupervised visual representation learning"
    - "domain generalization by solving jigsaw puzzles"
    - "bridging theory and algorithm for domain adaptation"
    - "local aggregation for unsupervised learning of visual embeddings"

Connected through hyperedge HE-3 (induced by source paper "domain adaptation with conditional distribution matching and generalized label shift"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "domain agnostic learning with disentangled representations"
    - "robust natural language inference models with example forgetting"
    - "batch weight for domain adaptation with mass shift"
    - "learning to transfer examples for partial domain adaptation"
    - "learning representations by maximizing mutual information across views"
    - "regularized learning for domain adaptation under label shifts"
    - "on learning invariant representation for domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "statistical guarantees of generative adversarial networks for distribution estimation"):
  with members:
    - "width provably matters in optimization for deep linear neural networks"
    - "improving generalization and stability of generative adversarial networks"
    - "efficient approximation of deep relu networks for functions on low dimensional manifolds"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "improving gan training via binarized representation entropy bre regularization"

Connected through hyperedge HE-5 (induced by source paper "large scale optimal transport via adversarial training with cycle consistency"):
  with members:
    - "on scalable and efficient computation of large scale optimal transport"
    - "adversarial computation of optimal transport maps"
    - "optimal transport mapping via input convex neural networks"
    - "scalable unbalanced optimal transport using generative adversarial networks"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"
    - "guiding the one to one mapping in cyclegan via optimal transport"
    - "cycada cycle consistent adversarial domain adaptation"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "geometric dataset distances via optimal transport"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "dynamics and reachability of learning tasks"
    - "hierarchical optimal transport for document representation"
    - "transferability and hardness of supervised classification tasks"
    - "task2vec task embedding for meta learning"
    - "learning embeddings into entropic wasserstein spaces"
    - "gromov wasserstein alignment of word embedding spaces"
    - "deep learning for classical japanese literature"

Connected through hyperedge HE-7 (induced by source paper "learning 2d 3d correspondences to solve the blind perspective n point problem"):
  with members:
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "the alignment of the spheres globally optimal spherical mixture alignment for camera pose estimation"
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "dynamic graph cnn for learning on point clouds"
    - "megadepth learning single view depth prediction from internet photos"
    - "geometric loss functions for camera pose regression with deep learning"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "learning to find good correspondences"

Connected through hyperedge HE-8 (induced by source paper "deep learning for learning graph representations"):
  with members:
    - "structural deep embedding for hyper networks"
    - "node2vec scalable feature learning for networks"
    - "deep residual learning for image recognition"
    - "role discovery in networks"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"
    - "word representations via gaussian embedding"

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.LG(Machine Learning)