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: "deep unsupervised similarity learning using partially ordered sets"
  Abstract: Unsupervised learning of visual similarities is of paramount importance to computer vision, particularly due to lacking training data for fine-grained similarities. Deep learning of similarities is often based on relationships between pairs or triplets of samples. Many of these relations are unreliable and mutually contradicting, implying inconsistencies when trained without supervision information that relates different tuples or triplets to each other. To overcome this problem, we use local estimates of reliable (dis-)similarities to initially group samples into compact surrogate classes and use local partial orders of samples to classes to link classes to each other. Similarity learning is then formulated as a partial ordering task with soft correspondences of all samples to classes. Adopting a strategy of self-supervision, a CNN is trained to optimally represent samples in a mutually consistent manner while updating the classes. The similarity learning and grouping procedure are integrated in a single model and optimized jointly. The proposed unsupervised approach shows competitive performance on detailed pose estimation and object classification.

Connected through hyperedge HE-1 (induced by source paper "unsupervised deep metric learning via auxiliary rotation loss"):
  with members:
    - "on mutual information maximization for representation learning"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "hardness aware deep metric learning"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "unsupervised pre training of image features on non curated data"
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "unsupervised embedding learning via invariant and spreading instance feature"

Connected through hyperedge HE-2 (induced by source paper "divide and conquer the embedding space for metric learning"):
  with members:
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "learning discriminative features with multiple granularities for person re identification"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "harmonious attention network for person re identification"
    - "deep randomized ensembles for metric learning"
    - "mining on manifolds metric learning without labels"
    - "deep group shuffling random walk for person re identification"
    - "deep metric learning with hierarchical triplet loss"

Connected through hyperedge HE-3 (induced by source paper "mic mining interclass characteristics for improved metric learning"):
  with members:
    - "divide and conquer the embedding space for metric learning"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "deep randomized ensembles for metric learning"
    - "cross and learn cross modal self supervision"
    - "mining on manifolds metric learning without labels"
    - "deep metric learning with hierarchical triplet loss"

Connected through hyperedge HE-4 (induced by source paper "hardness aware deep metric learning"):
  with members:
    - "hard aware point to set deep metric for person re identification"
    - "deep randomized ensembles for metric learning"
    - "deep metric learning with hierarchical triplet loss"
    - "attention based ensemble for deep metric learning"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "deep metric learning with angular loss"
    - "no fuss distance metric learning using proxies"
    - "smart mining for deep metric learning"

Connected through hyperedge HE-5 (induced by source paper "semi supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "sfa gtm seismic facies analysis based on generative topographic map and rbf"
    - "automatic salt deposits segmentation a deep learning approach"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "deep unsupervised learning of visual similarities"
    - "unsupervised representation learning by sorting sequences"

Connected through hyperedge HE-6 (induced by source paper "information geometric set embeddings igse from sets to probability distributions"):
  with members:
    - "on the jensen shannon symmetrization of distances relying on abstract means"
    - "attention based deep multiple instance learning"
    - "deep sets"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "node2vec scalable feature learning for networks"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "mining on manifolds metric learning without labels"):
  with members:
    - "nearest neighbour radial basis function solvers for deep neural networks"
    - "polysemous codes"
    - "parametric manifold learning via sparse multidimensional scaling"
    - "sampling matters in deep embedding learning"
    - "transitive invariance for self supervised visual representation learning"
    - "self supervised learning of visual features through embedding images into text topic spaces"
    - "iterative manifold embedding layer learned by incomplete data for large scale image retrieval"
    - "multi task self supervised visual learning"

Connected through hyperedge HE-8 (induced by source paper "video jigsaw unsupervised learning of spatiotemporal context for video action recognition"):
  with members:
    - "transitive invariance for self supervised visual representation learning"
    - "what actions are needed for understanding human actions in videos"
    - "unsupervised video understanding by reconciliation of posture similarities"
    - "multi task self supervised visual learning"
    - "self supervised learning of pose embeddings from spatiotemporal relations in videos"
    - "the kinetics human action video dataset"
    - "unsupervised representation learning by sorting sequences"
    - "cross domain self supervised multi task feature learning using synthetic imagery"

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)