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: "local naive bayes nearest neighbor for image classification"
  Abstract: We present Local Naive Bayes Nearest Neighbor, an improvement to the NBNN image classification algorithm that increases classification accuracy and improves its ability to scale to large numbers of object classes. The key observation is that only the classes represented in the local neighborhood of a descriptor contribute significantly and reliably to their posterior probability estimates. Instead of maintaining a separate search structure for each class, we merge all of the reference data together into one search structure, allowing quick identification of a descriptor's local neighborhood. We show an increase in classification accuracy when we ignore adjustments to the more distant classes and show that the run time grows with the log of the number of classes rather than linearly in the number of classes as did the original. This gives a 100 times speed-up over the original method on the Caltech 256 dataset. We also provide the first head-to-head comparison of NBNN against spatial pyramid methods using a common set of input features. We show that local NBNN outperforms all previous NBNN based methods and the original spatial pyramid model. However, we find that local NBNN, while competitive with, does not beat state-of-the-art spatial pyramid methods that use local soft assignment and max-pooling.

Connected through hyperedge HE-1 (induced by source paper "towards universal representation for unseen action recognition"):
  with members:
    - "deep texture manifold for ground terrain recognition"
    - "alternative semantic representations for zero shot human action recognition"
    - "deep local video feature for action recognition"
    - "hidden two stream convolutional networks for action recognition"
    - "densenet for dense flow"
    - "the kinetics human action video dataset"
    - "guided optical flow learning"
    - "image de raining using a conditional generative adversarial network"

Connected through hyperedge HE-2 (induced by source paper "deep micro dictionary learning and coding network"):
  with members:
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "convolutional dictionary learning acceleration and convergence"
    - "structure aware classification using supervised dictionary learning"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "greedy deep dictionary learning"
    - "discriminative bayesian dictionary learning for classification"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "convolutional clustering for unsupervised learning"

Connected through hyperedge HE-3 (induced by source paper "integral curvature representation and matching algorithms for identification of dolphins and whales"):
  with members:
    - "automated visual fin identification of individual great white sharks"
    - "spatial transformer networks"
    - "how transferable are features in deep neural networks"
    - "learning fine grained image similarity with deep ranking"
    - "deep metric learning using triplet network"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "automated visual fin identification of individual great white sharks"):
  with members:
    - "simultaneous detection and segmentation"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "learning a metric for class conditional knn"):
  with members:
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "regularization and kernelization of the maximin correlation approach"):
  with members:
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"

Connected through hyperedge HE-7 (induced by source paper "exemplar based deep discriminative and shareable feature learning for scene image classification"):
  with members:
    - "a spatial layout and scale invariant feature representation for indoor scene classification"
    - "a discriminative representation of convolutional features for indoor scene recognition"
    - "inverting visual representations with convolutional networks"
    - "a novel feature extraction method for scene recognition based on centered convolutional restricted boltzmann machines"
    - "natural scene recognition based on superpixels and deep boltzmann machines"
    - "max margin based discriminative feature learning"
    - "unsupervised feature learning with c svddnet"
    - "multi scale orderless pooling of deep convolutional activation features"

Connected through hyperedge HE-8 (induced by source paper "large margin learning of compact binary image encodings"):
  with members:
    - "learning hash functions using column generation"
    - "structboost boosting methods for predicting structured output variables"
    - "positive semidefinite metric learning using boosting like algorithms"
    - "incremental training of a detector using online sparse eigendecomposition"
    - "self taught hashing for fast similarity search"

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)