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: "learning topic models going beyond svd"
  Abstract: Topic Modeling is an approach used for automatic comprehension and classification of data in a variety of settings, and perhaps the canonical application is in uncovering thematic structure in a corpus of documents. A number of foundational works both in machine learning and in theory have suggested a probabilistic model for documents, whereby documents arise as a convex combination of (i.e. distribution on) a small number of topic vectors, each topic vector being a distribution on words (i.e. a vector of word-frequencies). Similar models have since been used in a variety of application areas; the Latent Dirichlet Allocation or LDA model of Blei et al. is especially popular. #R##N#Theoretical studies of topic modeling focus on learning the model's parameters assuming the data is actually generated from it. Existing approaches for the most part rely on Singular Value Decomposition(SVD), and consequently have one of two limitations: these works need to either assume that each document contains only one topic, or else can only recover the span of the topic vectors instead of the topic vectors themselves. #R##N#This paper formally justifies Nonnegative Matrix Factorization(NMF) as a main tool in this context, which is an analog of SVD where all vectors are nonnegative. Using this tool we give the first polynomial-time algorithm for learning topic models without the above two limitations. The algorithm uses a fairly mild assumption about the underlying topic matrix called separability, which is usually found to hold in real-life data. A compelling feature of our algorithm is that it generalizes to models that incorporate topic-topic correlations, such as the Correlated Topic Model and the Pachinko Allocation Model. #R##N#We hope that this paper will motivate further theoretical results that use NMF as a replacement for SVD - just as NMF has come to replace SVD in many applications.

Connected through hyperedge HE-1 (induced by source paper "contrastive estimation reveals topic posterior information to linear models"):
  with members:
    - "overparameterized neural networks can implement associative memory"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an efficient framework for learning sentence representations"
    - "character level convolutional networks for text classification"
    - "unsupervised learning of visual representations using videos"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "learning nonlinear dynamic models"

Connected through hyperedge HE-2 (induced by source paper "processing topical queries on images of historical newspaper pages"):
  with members:
    - "low dimensional embeddings for interpretable anchor based topic inference"

Connected through hyperedge HE-3 (induced by source paper "finding a latent k simplex in o k nnz data time via subset smoothing"):
  with members:
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "a tensor approach to learning mixed membership community models"
    - "the noisy power method a meta algorithm with applications"
    - "low rank approximation and regression in input sparsity time"
    - "improved spectral norm bounds for clustering"
    - "efficient learning of simplices"
    - "a spectral algorithm for latent dirichlet allocation"

Connected through hyperedge HE-4 (induced by source paper "compressed factorization fast and accurate low rank factorization of compressively sensed data"):
  with members:
    - "a practical randomized cp tensor decomposition"
    - "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use"
    - "compressed nonnegative matrix factorization is fast and accurate"
    - "stable robust and super fast reconstruction of tensors using multi way projections"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "low rank approximation and regression in input sparsity time"
    - "exact recovery of sparsely used dictionaries"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-5 (induced by source paper "generalized separable nonnegative matrix factorization"):
  with members:
    - "on identifiability of nonnegative matrix factorization"
    - "hierarchical clustering of hyperspectral images using rank two nonnegative matrix factorization"
    - "learning the information divergence"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "computing a nonnegative matrix factorization provably"
    - "on the complexity of nonnegative matrix factorization"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-6 (induced by source paper "sample optimal parametric q learning using linearly additive features"):
  with members:
    - "state aggregation learning from markov transition data"
    - "scalable bilinear pi learning using state and action features"
    - "variance reduced value iteration and faster algorithms for solving markov decision processes"
    - "reinforcement learning in rich observation mdps using spectral methods"
    - "contextual decision processes with low bellman rank are pac learnable"

Connected through hyperedge HE-7 (induced by source paper "a quantum inspired classical algorithm for separable non negative matrix factorization"):
  with members:
    - "a quantum inspired classical algorithm for recommendation systems"
    - "quantum inspired low rank stochastic regression with logarithmic dependence on the dimension"
    - "quantum inspired sublinear classical algorithms for solving low rank linear systems"
    - "computing a nonnegative matrix factorization provably"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-8 (induced by source paper "classifying topics in speech when all you have is crummy translations"):
  with members:
    - "visually grounded cross lingual keyword spotting in speech"
    - "vision as an interlingua learning multilingual semantic embeddings of untranscribed speech"
    - "fast asr free and almost zero resource keyword spotting using dtw and cnns for humanitarian monitoring"
    - "towards speech to text translation without speech recognition"

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)