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: "deep learning sparse ternary projections for compressed sensing of images"
  Abstract: Compressed sensing (CS) is a sampling theory that allows reconstruction of sparse (or compressible) signals from an incomplete number of measurements, using of a sensing mechanism implemented by an appropriate projection matrix. The CS theory is based on random Gaussian projection matrices, which satisfy recovery guarantees with high probability; however, sparse ternary {0, -1, +1} projections are more suitable for hardware implementation. In this paper, we present a deep learning approach to obtain very sparse ternary projections for compressed sensing. Our deep learning architecture jointly learns a pair of a projection matrix and a reconstruction operator in an end-to-end fashion. The experimental results on real images demonstrate the effectiveness of the proposed approach compared to state-of-the-art methods, with significant advantage in terms of complexity.

Connected through hyperedge HE-1 (induced by source paper "jointly sparse signal recovery via deep auto encoder and parallel coordinate descent unrolling"):
  with members:
    - "improved scaling law for activity detection in massive mimo systems"
    - "grant free massive mtc enabled massive mimo a compressive sensing approach"
    - "sparse activity detection for massive connectivity"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "deep networks for compressed image sensing"
    - "deep learning for massive mimo csi feedback"

Connected through hyperedge HE-2 (induced by source paper "deep coupled representation learning for sparse linear inverse problems with side information"):
  with members:
    - "an approximate message passing framework for side information"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "multimodal image super resolution via joint sparse representations induced by coupled dictionaries"
    - "multi modal dictionary learning for image separation with application in art investigation"
    - "tradeoffs between convergence speed and reconstruction accuracy in inverse problems"
    - "from denoising to compressed sensing"
    - "deep unfolding model based inspiration of novel deep architectures"

Connected through hyperedge HE-3 (induced by source paper "joint design of measurement matrix and sparse support recovery method via deep auto encoder"):
  with members:
    - "deep compressed sensing"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "deepiot compressing deep neural network structures for sensing systems with a compressor critic framework"
    - "deep learning for massive mimo csi feedback"
    - "adam a method for stochastic optimization"
    - "subspace methods for joint sparse recovery"

Connected through hyperedge HE-4 (induced by source paper "regularizing autoencoder based matrix completion models via manifold learning"):
  with members:
    - "twitter user geolocation using deep multiview learning"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "an overview of multi task learning in deep neural networks"
    - "hybrid collaborative filtering with neural networks"
    - "hybrid collaborative filtering with autoencoders"
    - "a neural autoregressive approach to collaborative filtering"
    - "hybrid recommender system based on autoencoders"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "twitter user geolocation using deep multiview learning"):
  with members:
    - "a neural model for user geolocation and lexical dialectology"
    - "node2vec scalable feature learning for networks"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "multiview deep learning for predicting twitter users location"):
  with members:
    - "end to end network for twitter geolocation prediction and hashing"
    - "a neural model for user geolocation and lexical dialectology"
    - "node2vec scalable feature learning for networks"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"
    - "addressing the rare word problem in neural machine translation"
    - "geotagging one hundred million twitter accounts with total variation minimization"

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)