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: "towards learning sparsely used dictionaries with arbitrary supports"
  Abstract: Dictionary learning is a popular approach for inferring a hidden basis or dictionary in which data has a sparse representation. Data generated from the dictionary A (an n by m matrix, with m > n in the over-complete setting) is given by Y = AX where X is a matrix whose columns have supports chosen from a distribution over k-sparse vectors, and the non-zero values chosen from a symmetric distribution. Given Y, the goal is to recover A and X in polynomial time. Existing algorithms give polytime guarantees for recovering incoherent dictionaries, under strong distributional assumptions both on the supports of the columns of X, and on the values of the non-zero entries. In this work, we study the following question: Can we design efficient algorithms for recovering dictionaries when the supports of the columns of X are arbitrary? #R##N#To address this question while circumventing the issue of non-identifiability, we study a natural semirandom model for dictionary learning where there are a large number of samples $y=Ax$ with arbitrary k-sparse supports for x, along with a few samples where the sparse supports are chosen uniformly at random. While the few samples with random supports ensures identifiability, the support distribution can look almost arbitrary in aggregate. Hence existing algorithmic techniques seem to break down as they make strong assumptions on the supports. #R##N#Our main contribution is a new polynomial time algorithm for learning incoherent over-complete dictionaries that works under the semirandom model. Additionally the same algorithm provides polynomial time guarantees in new parameter regimes when the supports are fully random. Finally using these techniques, we also identify a minimal set of conditions on the supports under which the dictionary can be (information theoretically) recovered from polynomial samples for almost linear sparsity, i.e., $k=\tilde{O}(n)$.

Connected through hyperedge HE-1 (induced by source paper "anchor transform learning sparse representations of discrete objects"):
  with members:
    - "neural input search for large scale recommendation models"
    - "how large a vocabulary does text classification need a variational approach to vocabulary selection"
    - "word embedding based on low rank doubly stochastic matrix decomposition"
    - "bayesian compression for natural language processing"
    - "probabilistic fasttext for multi sense word embeddings"
    - "adaptive input representations for neural language modeling"
    - "attention is all you need"
    - "learning k way d dimensional discrete code for compact embedding representations"

Connected through hyperedge HE-2 (induced by source paper "approximate guarantees for dictionary learning"):
  with members:
    - "sparse approximation via generating point sets"
    - "on the computational intractability of exact and approximate dictionary learning"
    - "variable selection is hard"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "hypercontractivity sum of squares proofs and their applications"
    - "exact recovery of sparsely used dictionaries"

Connected through hyperedge HE-3 (induced by source paper "subgradient descent learns orthogonal dictionaries"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "rate optimal denoising with deep neural networks"
    - "nonconvex robust low rank matrix recovery"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "learning relu networks via alternating minimization"
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "deep denoising rate optimal recovery of structured signals with a deep prior"

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)