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: "robust hashing for multi view data jointly learning low rank kernelized similarity consensus and hash functions"
  Abstract: Learning hash functions/codes for similarity search over multi-view data is attracting increasing attention, where similar hash codes are assigned to the data objects characterizing consistently neighborhood relationship across views. Traditional methods in this category inherently suffer three limitations: 1) they commonly adopt a two-stage scheme where similarity matrix is first constructed, followed by a subsequent hash function learning; 2) these methods are commonly developed on the assumption that data samples with multiple representations are noise-free,which is not practical in real-life applications; 3) they often incur cumbersome training model caused by the neighborhood graph construction using all $N$ points in the database ($O(N)$). In this paper, we motivate the problem of jointly and efficiently training the robust hash functions over data objects with multi-feature representations which may be noise corrupted. To achieve both the robustness and training efficiency, we propose an approach to effectively and efficiently learning low-rank kernelized \footnote{We use kernelized similarity rather than kernel, as it is not a squared symmetric matrix for data-landmark affinity matrix.} hash functions shared across views. Specifically, we utilize landmark graphs to construct tractable similarity matrices in multi-views to automatically discover neighborhood structure in the data. To learn robust hash functions, a latent low-rank kernel function is used to construct hash functions in order to accommodate linearly inseparable data. In particular, a latent kernelized similarity matrix is recovered by rank minimization on multiple kernel-based similarity matrices. Extensive experiments on real-world multi-view datasets validate the efficacy of our method in the presence of error corruptions.

Connected through hyperedge HE-1 (induced by source paper "a convolutional neural networks denoising approach for salt and pepper noise"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "beyond low rank representations orthogonal clustering basis reconstruction with optimized graph structure for multi view spectral clustering"
    - "deep reconstruction classification networks for unsupervised domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "robust tracking via weighted online extreme learning machine"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "beyond low rank representations orthogonal clustering basis reconstruction with optimized graph structure for multi view spectral clustering"
    - "structured deep hashing with convolutional neural networks for fast person re identification"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"

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.LG(Machine Learning)