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: "on the relationship between sum product networks and bayesian networks"
  Abstract: In this paper, we establish some theoretical connections between Sum-Product Networks (SPNs) and Bayesian Networks (BNs). We prove that every SPN can be converted into a BN in linear time and space in terms of the network size. The key insight is to use Algebraic Decision Diagrams (ADDs) to compactly represent the local conditional probability distributions at each node in the resulting BN by exploiting context-specific independence (CSI). The generated BN has a simple directed bipartite graphical structure. We show that by applying the Variable Elimination algorithm (VE) to the generated BN with ADD representations, we can recover the original SPN where the SPN can be viewed as a history record or caching of the VE inference process. To help state the proof clearly, we introduce the notion of {\em normal} SPN and present a theoretical analysis of the consistency and decomposability properties. We conclude the paper with some discussion of the implications of the proof and establish a connection between the depth of an SPN and a lower bound of the tree-width of its corresponding BN.

Connected through hyperedge HE-1 (induced by source paper "interventions and counterfactuals in tractable probabilistic models limitations of contemporary transformations"):
  with members:
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "counterfactuals uncover the modular structure of deep generative models"
    - "on relaxing determinism in arithmetic circuits"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "budgeted experiment design for causal structure learning"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "on the latent variable interpretation in sum product networks"
    - "a differential approach to inference in bayesian networks"

Connected through hyperedge HE-2 (induced by source paper "cryptospn privacy preserving sum product network inference"):
  with members:
    - "spflow an easy and extensible library for deep probabilistic learning using sum product networks"
    - "sgxpectre stealing intel secrets from sgx enclaves via speculative execution"
    - "gazelle a low latency framework for secure neural network inference"
    - "on relaxing determinism in arithmetic circuits"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-3 (induced by source paper "visualizing and understanding sum product networks"):
  with members:
    - "on relaxing determinism in arithmetic circuits"
    - "approximation complexity of maximum a posteriori inference in sum product networks"
    - "on the latent variable interpretation in sum product networks"
    - "learning tractable probabilistic models for fault localization"
    - "understanding neural networks through deep visualization"
    - "made masked autoencoder for distribution estimation"
    - "visualizing and understanding recurrent networks"
    - "on the expressive efficiency of sum product networks"

Connected through hyperedge HE-4 (induced by source paper "bayesian learning of sum product networks"):
  with members:
    - "spflow an easy and extensible library for deep probabilistic learning using sum product networks"
    - "optimisation of overparametrized sum product networks"
    - "probabilistic deep learning using random sum product networks"
    - "tensorial mixture models"
    - "on the latent variable interpretation in sum product networks"
    - "a unified approach for learning the parameters of sum product networks"
    - "neural architecture search with reinforcement learning"
    - "learning bayesian networks the combination of knowledge and statistical data"

Connected through hyperedge HE-5 (induced by source paper "deep structured mixtures of gaussian processes"):
  with members:
    - "bayesian learning of sum product networks"
    - "optimisation of overparametrized sum product networks"
    - "on the latent variable interpretation in sum product networks"
    - "generalized product of experts for automatic and principled fusion of gaussian process predictions"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-6 (induced by source paper "sum product network decompilation"):
  with members:
    - "probabilistic deep learning using random sum product networks"
    - "on relaxing determinism in arithmetic circuits"
    - "context specific independence in bayesian networks"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-7 (induced by source paper "probabilistic deep learning using random sum product networks"):
  with members:
    - "on the latent variable interpretation in sum product networks"
    - "pixel recurrent neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "a differential approach to inference in bayesian networks"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-8 (induced by source paper "maximum a posteriori inference in sum product networks"):
  with members:
    - "on relaxing determinism in arithmetic circuits"
    - "approximation complexity of maximum a posteriori inference in sum product networks"
    - "on the latent variable interpretation in sum product networks"
    - "a differential approach to inference in bayesian networks"
    - "learning arithmetic circuits"
    - "sum product networks a new deep architecture"
    - "map complexity results and approximation methods"
    - "a knowledge compilation map"

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.AI(Artificial Intelligence)