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: "hierarchical compositional feature learning"
  Abstract: We introduce the hierarchical compositional network (HCN), a directed generative model able to discover and disentangle, without supervision, the building blocks of a set of binary images. The building blocks are binary features defined hierarchically as a composition of some of the features in the layer immediately below, arranged in a particular manner. At a high level, HCN is similar to a sigmoid belief network with pooling. Inference and learning in HCN are very challenging and existing variational approximations do not work satisfactorily. A main contribution of this work is to show that both can be addressed using max-product message passing (MPMP) with a particular schedule (no EM required). Also, using MPMP as an inference engine for HCN makes new tasks simple: adding supervision information, classifying images, or performing inpainting all correspond to clamping some variables of the model to their known values and running MPMP on the rest. When used for classification, fast inference with HCN has exactly the same functional form as a convolutional neural network (CNN) with linear activations and binary weights. However, HCN’s features are qualitatively very different.

Connected through hyperedge HE-1 (induced by source paper "hierarchical quantized autoencoders"):
  with members:
    - "exact rate distortion in autoencoders via echo noise"
    - "on the variance of the adaptive learning rate and beyond"
    - "hierarchical autoregressive image models with auxiliary decoders"
    - "rethinking lossy compression the rate distortion perception tradeoff"
    - "the perception distortion tradeoff"
    - "deep generative models for distribution preserving lossy compression"
    - "recent advances in autoencoder based representation learning"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "toyarchitecture unsupervised learning of interpretable models of the world"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "a neurally inspired hierarchical prediction network for spatiotemporal sequence learning and prediction"
    - "unsupervised predictive memory in a goal directed agent"
    - "the consciousness prior"
    - "a simple neural network module for relational reasoning"
    - "cortexnet a generic network family for robust visual temporal representations"
    - "dynamic routing between capsules"
    - "neural symbolic learning and reasoning a survey and interpretation"

Connected through hyperedge HE-3 (induced by source paper "what can the brain teach us about building artificial intelligence"):
  with members:
    - "building machines that learn and think like people"

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)