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: "fast approximations to structured sparse coding and applications to object classification"
  Abstract: We describe a method for fast approximation of sparse coding. The input space is subdivided by a binary decision tree, and we simultaneously learn a dictionary and assignment of allowed dictionary elements for each leaf of the tree. We store a lookup table with the assignments and the pseudoinverses for each node, allowing for very fast inference. We give an algorithm for learning the tree, the dictionary and the dictionary element assignment, and In the process of describing this algorithm, we discuss the more general problem of learning the groups in group structured sparse modelling. We show that our method creates good sparse representations by using it in the object recognition framework of \cite{lazebnik06,yang-cvpr-09}. Implementing our own fast version of the SIFT descriptor the whole system runs at 20 frames per second on $321 \times 481$ sized images on a laptop with a quad-core cpu, while sacrificing very little accuracy on the Caltech 101 and 15 scenes benchmarks.

Connected through hyperedge HE-1 (induced by source paper "orthogonal matching pursuit for text classification"):
  with members:
    - "group sparse cnns for question classification with answer sets"
    - "stabilizing training of generative adversarial networks through regularization"
    - "linguistically regularized lstms for sentiment classification"
    - "distributed representations of words and phrases and their compositionality"
    - "get out the vote determining support or opposition from congressional floor debate transcripts"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"

Connected through hyperedge HE-2 (induced by source paper "discriminative nonlinear analysis operator learning when cosparse model meets image classification"):
  with members:
    - "joint sparse recovery with semisupervised music"
    - "sparse modeling for image and vision processing"
    - "dictionary learning for fast classification based on soft thresholding"
    - "analysis operator learning and its application to image reconstruction"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "binary embeddings with structured hashed projections"):
  with members:
    - "triplespin a generic compact paradigm for fast machine learning computations"
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "compressing neural networks with the hashing trick"
    - "binary embedding fundamental limits and fast algorithm"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "on binary embedding using circulant matrices"
    - "exploiting linear structure within convolutional networks for efficient evaluation"
    - "the loss surfaces of multilayer networks"

Connected through hyperedge HE-4 (induced by source paper "a field guide to forward backward splitting with a fasta implementation"):
  with members:
    - "algorithms initializations and convergence for the nonnegative matrix factorization"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "anti sparse coding for approximate nearest neighbor search"
    - "low rank matrix completion with noisy observations a quantitative comparison"

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)