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: "an equivalence of fully connected layer and convolutional layer"
  Abstract: This article demonstrates that convolutional operation can be converted to matrix multiplication, which has the same calculation way with fully connected layer. The article is helpful for the beginners of the neural network to understand how fully connected layer and the convolutional layer work in the backend. To be concise and to make the article more readable, we only consider the linear case. It can be extended to the non-linear case easily through plugging in a non-linear encapsulation to the values like this $\sigma(x)$ denoted as $x^{\prime}$.

Connected through hyperedge HE-1 (induced by source paper "practical and bilateral privacy preserving federated learning"):
  with members:
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "advances and open problems in federated learning"
    - "privacy preserving federated brain tumour segmentation"
    - "communication efficient federated deep learning with asynchronous model update and temporally weighted aggregation"
    - "semi cyclic stochastic gradient descent"
    - "federated learning for healthcare informatics"
    - "federated learning challenges methods and future directions"
    - "agnostic federated learning"

Connected through hyperedge HE-2 (induced by source paper "bayesnas a bayesian approach for neural architecture search"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "snip single shot network pruning based on connection sensitivity"

Connected through hyperedge HE-3 (induced by source paper "compnet neural networks growing via the compact network morphism"):
  with members:
    - "on weight initialization in deep neural networks"
    - "neural optimizer search with reinforcement learning"
    - "machine learning modeling for time series problem predicting flight ticket prices"
    - "a survey of model compression and acceleration for deep neural networks"
    - "channel pruning for accelerating very deep neural networks"
    - "skip connections as effective symmetry breaking"
    - "deep residual learning for image recognition"
    - "net2net accelerating learning via knowledge transfer"

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)