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: "learning to discover novel visual categories via deep transfer clustering"
  Abstract: We consider the problem of discovering novel object categories in an image collection. While these images are unlabelled, we also assume prior knowledge of related but different image classes. We use such prior knowledge to reduce the ambiguity of clustering, and improve the quality of the newly discovered classes. Our contributions are twofold. The first contribution is to extend Deep Embedded Clustering to a transfer learning setting; we also improve the algorithm by introducing a representation bottleneck, temporal ensembling, and consistency. The second contribution is a method to estimate the number of classes in the unlabelled data. This also transfers knowledge from the known classes, using them as probes to diagnose different choices for the number of classes in the unlabelled subset. We thoroughly evaluate our method, substantially outperforming state-of-the-art techniques in a large number of benchmarks, including ImageNet, OmniGlot, CIFAR-100, CIFAR-10, and SVHN.

Connected through hyperedge HE-1 (induced by source paper "automatically discovering and learning new visual categories with ranking statistics"):
  with members:
    - "multi class classification without multi class labels"
    - "semi supervised learning with scarce annotations"
    - "revisiting self supervised visual representation learning"
    - "a survey on deep transfer learning"
    - "unsupervised representation learning by predicting image rotations"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "memory aware synapses learning what not to forget"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-2 (induced by source paper "exploring unlabeled faces for novel attribute discovery"):
  with members:
    - "large scale object mining for object discovery from unlabeled video"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "arcface additive angular margin loss for deep face recognition"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "object discovery in videos as foreground motion clustering"

Connected through hyperedge HE-3 (induced by source paper "no representation without transformation"):
  with members:
    - "unsupervised learning of object keypoints for perception and control"
    - "dynamics aware unsupervised discovery of skills"
    - "temporal cycle consistency learning"
    - "shaping belief states with generative environment models for rl"
    - "neural processes"
    - "neural predictive belief representations"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "towards a definition of disentangled representations"

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)