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: "unsupervised deep learning by neighbourhood discovery"
  Abstract: Deep convolutional neural networks (CNNs) have demonstrated remarkable success in computer vision by supervisedly learning strong visual feature representations. However, training CNNs relies heavily on the availability of exhaustive training data annotations, limiting significantly their deployment and scalability in many application scenarios. In this work, we introduce a generic unsupervised deep learning approach to training deep models without the need for any manual label supervision. Specifically, we progressively discover sample anchored/centred neighbourhoods to reason and learn the underlying class decision boundaries iteratively and accumulatively. Every single neighbourhood is specially formulated so that all the member samples can share the same unseen class labels at high probability for facilitating the extraction of class discriminative feature representations during training. Experiments on image classification show the performance advantages of the proposed method over the state-of-the-art unsupervised learning models on six benchmarks including both coarse-grained and fine-grained object image categorisation.

Connected through hyperedge HE-1 (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"
    - "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"
    - "semi supervised domain adaptation via minimax entropy"

Connected through hyperedge HE-2 (induced by source paper "universal domain adaptation through self supervision"):
  with members:
    - "momentum contrast for unsupervised visual representation learning"
    - "domain specific batch normalization for unsupervised domain adaptation"
    - "domain generalization by solving jigsaw puzzles"
    - "bridging theory and algorithm for domain adaptation"
    - "learning to transfer examples for partial domain adaptation"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "semi supervised domain adaptation via minimax entropy"
    - "cluster alignment with a teacher for unsupervised domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "a comprehensive approach to unsupervised embedding learning based on and algorithm"):
  with members:
    - "unsupervised embedding learning via invariant and spreading instance feature"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised representation learning by predicting image rotations"
    - "invariant information clustering for unsupervised image classification and segmentation"
    - "deep unsupervised clustering using mixture of autoencoders"
    - "representation learning by learning to count"
    - "temporal ensembling for semi supervised learning"
    - "split brain autoencoders unsupervised learning by cross channel prediction"

Connected through hyperedge HE-4 (induced by source paper "self labelling via simultaneous clustering and representation learning"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "surprising effectiveness of few image unsupervised feature learning"
    - "data efficient image recognition with contrastive predictive coding"
    - "a critical analysis of self supervision or what we can learn from a single image"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "learning representations by maximizing mutual information across views"
    - "revisiting self supervised visual representation learning"
    - "self supervised learning of pretext invariant representations"

Connected through hyperedge HE-5 (induced by source paper "csnns unsupervised backpropagation free convolutional neural networks for representation learning"):
  with members:
    - "locally connected spiking neural networks for unsupervised feature learning"
    - "unsupervised learning by competing hidden units"
    - "large scale adversarial representation learning"
    - "sparsely activated networks"
    - "unsupervised discovery of parts structure and dynamics"
    - "evolving losses for unlabeled video representation learning"
    - "revisiting self supervised visual representation learning"
    - "unsupervised embedding learning via invariant and spreading instance feature"

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)