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: "transductive label augmentation for improved deep network learning"
  Abstract: A major impediment to the application of deep learning to real-world problems is the scarcity of labeled data. Small training sets are in fact of no use to deep networks as, due to the large number of trainable parameters, they will very likely be subject to overfitting phenomena. On the other hand, the increment of the training set size through further manual or semi-automatic labellings can be costly, if not possible at times. Thus, the standard techniques to address this issue are transfer learning and data augmentation, which consists of applying some sort of "transformation" to existing labeled instances to let the training set grow in size. Although this approach works well in applications such as image classification, where it is relatively simple to design suitable transformation operators, it is not obvious how to apply it in more structured scenarios. Motivated by the observation that in virtually all application domains it is easy to obtain unlabeled data, in this paper we take a different perspective and propose a \emph{label augmentation} approach. We start from a small, curated labeled dataset and let the labels propagate through a larger set of unlabeled data using graph transduction techniques. This allows us to naturally use (second-order) similarity information which resides in the data, a source of information which is typically neglected by standard augmentation techniques. In particular, we show that by using known game theoretic transductive processes we can create larger and accurate enough labeled datasets which use results in better trained neural networks. Preliminary experiments are reported which demonstrate a consistent improvement over standard image classification datasets.

Connected through hyperedge HE-1 (induced by source paper "constrained dominant sets and its applications in computer vision"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "deep constrained dominant sets for person re identification"
    - "weakly supervised semantic segmentation using constrained dominant sets"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning neural models for end to end clustering"
    - "multi feature fusion for image retrieval using constrained dominant sets"
    - "deep group shuffling random walk for person re identification"

Connected through hyperedge HE-2 (induced by source paper "the group loss for deep metric learning"):
  with members:
    - "deep constrained dominant sets for person re identification"
    - "ranked list loss for deep metric learning"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "learning with average precision training image retrieval with a listwise loss"
    - "relational knowledge distillation"
    - "divide and conquer the embedding space for metric learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "classification is a strong baseline for deep metric learning"

Connected through hyperedge HE-3 (induced by source paper "label propagation for deep semi supervised learning"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "mining on manifolds metric learning without labels"
    - "deep co training for semi supervised image recognition"
    - "unsupervised representation learning by predicting image rotations"
    - "dynamic few shot visual learning without forgetting"
    - "low shot learning with large scale diffusion"
    - "transitive invariance for self supervised visual representation learning"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-4 (induced by source paper "unsupervised domain adaptation using graph transduction games"):
  with members:
    - "ancient coin classification using graph transduction games"
    - "associative domain adaptation"
    - "deep transfer learning with joint adaptation networks"
    - "context aware nonnegative matrix factorization clustering"
    - "unsupervised domain adaptation with residual transfer networks"
    - "return of frustratingly easy domain adaptation"
    - "learning transferable features with deep adaptation networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "deep learning in the wild"):
  with members:
    - "deep watershed detector for music object recognition"
    - "deepscores a dataset for segmentation detection and classification of tiny objects"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "a survey of distributed message broker queues"
    - "emergent complexity via multi agent competition"
    - "fine grained recognition in the wild a multi task domain adaptation approach"

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)