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: "finding archetypal spaces for data using neural networks"
  Abstract: Archetypal analysis is a type of factor analysis where data is fit by a convex polytope whose corners are "archetypes" of the data, with the data represented as a convex combination of these archetypal points. While archetypal analysis has been used on biological data, it has not achieved widespread adoption because most data are not well fit by a convex polytope in either the ambient space or after standard data transformations. We propose a new approach to archetypal analysis. Instead of fitting a convex polytope directly on data or after a specific data transformation, we train a neural network (AAnet) to learn a transformation under which the data can best fit into a polytope. We validate this approach on synthetic data where we add nonlinearity. Here, AAnet is the only method that correctly identifies the archetypes. We also demonstrate AAnet on two biological datasets. In a T cell dataset measured with single cell RNA-sequencing, AAnet identifies several archetypal states corresponding to naive, memory, and cytotoxic T cells. In a dataset of gut microbiome profiles, AAnet recovers both previously described microbiome states and identifies novel extrema in the data. Finally, we show that AAnet has generative properties allowing us to uniformly sample from the data geometry even when the input data is not uniformly distributed.

Connected through hyperedge HE-1 (induced by source paper "deep archetypal analysis"):
  with members:
    - "deep variational information bottleneck"
    - "deep feature consistent variational autoencoder"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "semi supervised learning with deep generative models"
    - "deep learning face attributes in the wild"

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)