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: "dissimilarity based sparse subset selection"
  Abstract: Finding an informative subset of a large collection of data points or models is at the center of many problems in computer vision, recommender systems, bio/health informatics as well as image and natural language processing. Given pairwise dissimilarities between the elements of a `source set' and a `target set,' we consider the problem of finding a subset of the source set, called representatives or exemplars, that can efficiently describe the target set. We formulate the problem as a row-sparsity regularized trace minimization problem. Since the proposed formulation is, in general, NP-hard, we consider a convex relaxation. The solution of our optimization finds representatives and the assignment of each element of the target set to each representative, hence, obtaining a clustering. We analyze the solution of our proposed optimization as a function of the regularization parameter. We show that when the two sets jointly partition into multiple groups, our algorithm finds representatives from all groups and reveals clustering of the sets. In addition, we show that the proposed framework can effectively deal with outliers. Our algorithm works with arbitrary dissimilarities, which can be asymmetric or violate the triangle inequality. To efficiently implement our algorithm, we consider an Alternating Direction Method of Multipliers (ADMM) framework, which results in quadratic complexity in the problem size. We show that the ADMM implementation allows to parallelize the algorithm, hence further reducing the computational time. Finally, by experiments on real-world datasets, we show that our proposed algorithm improves the state of the art on the two problems of scene categorization using representative images and time-series modeling and segmentation using representative~models.

Connected through hyperedge HE-1 (induced by source paper "a multi criteria approach for fast and outlier aware representative selection from manifolds"):
  with members:
    - "salient object detection a discriminative regional feature integration approach"
    - "greedy column subset selection for large scale data sets"
    - "robust multiple manifolds structure learning"
    - "randomized algorithms for matrices and data"
    - "robust principal component analysis"
    - "an improved approximation algorithm for the column subset selection problem"

Connected through hyperedge HE-2 (induced by source paper "coin a large scale dataset for comprehensive instructional video analysis"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "end to end dense video captioning with masked transformer"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "weakly supervised video object grounding from text by loss weighting and object interaction"
    - "unsupervised visual linguistic reference resolution in instructional videos"
    - "diversity aware multi video summarization"
    - "action sets weakly supervised action segmentation without ordering constraints"
    - "temporal action detection with structured segment networks"

Connected through hyperedge HE-3 (induced by source paper "video skimming taxonomy and comprehensive survey"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "a general framework for edited video and raw video summarization"
    - "phd gifs personalized highlight detection for automatic gif creation"
    - "discovery of shared semantic spaces for multiscene video query and summarization"
    - "summarizing first person videos from third persons points of views"
    - "video summarization with attention based encoder decoder networks"
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "collaborative summarization of topic related videos"

Connected through hyperedge HE-4 (induced by source paper "prolfa representative prototype selection for local feature aggregation"):
  with members:
    - "second order democratic aggregation"
    - "learning local feature aggregation functions with backpropagation"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "an autoencoder approach to learning bilingual word representations"

Connected through hyperedge HE-5 (induced by source paper "scaled simplex representation for subspace clustering"):
  with members:
    - "ell_0 motivated low rank sparse subspace clustering"
    - "robust graph learning from noisy data"
    - "low rank kernel learning for graph based clustering"
    - "discriminative regression machine a classifier for high dimensional data or imbalanced data"
    - "clustering with similarity preserving"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "discriminative transformation learning for fuzzy sparse subspace clustering"
    - "external prior guided internal prior learning for real world noisy image denoising"

Connected through hyperedge HE-6 (induced by source paper "image segmentation using sparse subset selection"):
  with members:
    - "fusionseg learning to combine motion and appearance for fully automatic segmention of generic objects in videos"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "image segmentation using hierarchical merge tree"
    - "laplacian pyramid reconstruction and refinement for semantic segmentation"
    - "conditional random fields as recurrent neural networks"
    - "lifting gis maps into strong geometric context for scene understanding"
    - "learning deconvolution network for semantic segmentation"
    - "pushing the boundaries of boundary detection using deep learning"

Connected through hyperedge HE-7 (induced by source paper "fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features"):
  with members:
    - "learning where to see a novel attention model for automated immunohistochemical scoring"
    - "her2 challenge contest a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues"
    - "dynamic routing between capsules"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "gland segmentation in colon histology images the glas challenge contest"
    - "u net convolutional networks for biomedical image segmentation"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "assessment of algorithms for mitosis detection in breast cancer histopathology images"

Connected through hyperedge HE-8 (induced by source paper "simplex representation for subspace clustering"):
  with members:
    - "deep subspace clustering networks"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "invariant scattering convolution networks"

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)