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: "a fast pc algorithm for high dimensional causal discovery with multi core pcs"
  Abstract: Discovering causal relationships from observational data is a crucial problem and it has applications in many research areas. The PC algorithm is the state-of-the-art constraint based method for causal discovery. However, runtime of the PC algorithm, in the worst-case, is exponential to the number of nodes (variables), and thus it is inefficient when being applied to high dimensional data, e.g., gene expression datasets. On another note, the advancement of computer hardware in the last decade has resulted in the widespread availability of multi-core personal computers. There is a significant motivation for designing a parallelized PC algorithm that is suitable for personal computers and does not require end users’ parallel computing knowledge beyond their competency in using the PC algorithm. In this paper, we develop parallel-PC, a fast and memory efficient PC algorithm using the parallel computing technique. We apply our method to a range of synthetic and real-world high dimensional datasets. Experimental results on a dataset from the DREAM 5 challenge show that the original PC algorithm could not produce any results after running more than 24 hours; meanwhile, our parallel-PC algorithm managed to finish within around 12 hours with a 4-core CPU computer, and less than six hours with a 8-core CPU computer. Furthermore, we integrate parallel-PC into a causal inference method for inferring miRNA-mRNA regulatory relationships. The experimental results show that parallel-PC helps improve both the efficiency and accuracy of the causal inference algorithm.

Connected through hyperedge HE-1 (induced by source paper "cupc cuda based parallel pc algorithm for causal structure learning on gpu"):
  with members:
    - "causal inference and causal explanation with background knowledge"
    - "fast computation of moore penrose inverse matrices"

Connected through hyperedge HE-2 (induced by source paper "causal confusion in imitation learning"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "monocular plan view networks for autonomous driving"
    - "causal inference via kernel deviance measures"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "isolating sources of disentanglement in variational autoencoders"
    - "dart noise injection for robust imitation learning"

Connected through hyperedge HE-3 (induced by source paper "a survey of learning causality with data problems and methods"):
  with members:
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "stable prediction across unknown environments"
    - "linked causal variational autoencoder for inferring paired spillover effects"
    - "causally regularized learning with agnostic data selection bias"
    - "using simpson s paradox to discover interesting patterns in behavioral data"
    - "feature selection a data perspective"
    - "attributed network embedding for learning in a dynamic environment"
    - "cost optimal learning of causal graphs"

Connected through hyperedge HE-4 (induced by source paper "causal discovery in the presence of missing data"):
  with members:
    - "causality refined diagnostic prediction"
    - "efficient algorithms for bayesian network parameter learning from incomplete data"
    - "kernel based conditional independence test and application in causal discovery"

Connected through hyperedge HE-5 (induced by source paper "a review on algorithms for constraint based causal discovery"):
  with members:
    - "from observational studies to causal rule mining"
    - "parallelpc an r package for efficient constraint based causal exploration"
    - "discovering cyclic causal models with latent variables a general sat based procedure"
    - "causal inference and causal explanation with background knowledge"
    - "incorporating causal prior knowledge as path constraints in bayesian networks and maximal ancestral graphs"
    - "adjacency faithfulness and conservative causal inference"
    - "on the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables"

Connected through hyperedge HE-6 (induced by source paper "parallelpc an r package for efficient constraint based causal exploration"):
  with members:
    - "from observational studies to causal rule mining"

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)