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: "adjacency faithfulness and conservative causal inference"
  Abstract: Most causal inference algorithms in the literature (e.g., Pearl (2000), Spirtes et al. (2000), Heckerman et al. (1999)) exploit an assumption usually referred to as the causal Faithfulness or Stability condition. In this paper, we highlight two components of the condition used in constraint-based algorithms, which we call "Adjacency-Faithfulness" and "Orientation-Faithfulness". We point out that assuming Adjacency-Faithfulness is true, it is in principle possible to test the validity of Orientation-Faithfulness. Based on this observation, we explore the consequence of making only the Adjacency-Faithfulness assumption. We show that the familiar PC algorithm has to be modified to be (asymptotically) correct under the weaker, Adjacency-Faithfulness assumption. Roughly the modified algorithm, called Conservative PC (CPC), checks whether Orientation-Faithfulness holds in the orientation phase, and if not, avoids drawing certain causal conclusions the PC algorithm would draw. However, if the stronger, standard causal Faithfulness condition actually obtains, the CPC algorithm is shown to output the same pattern as the PC algorithm does in the large sample limit. We also present a simulation study showing that the CPC algorithm runs almost as fast as the PC algorithm, and outputs significantly fewer false causal arrowheads than the PC algorithm does on realistic sample sizes. We end our paper by discussing how score-based algorithms such as GES perform when the Adjacency-Faithfulness but not the standard causal Faithfulness condition holds, and how to extend our work to the FCI algorithm, which allows for the possibility of latent variables.

Connected through hyperedge HE-1 (induced by source paper "evaluating structure learning algorithms with a balanced scoring function"):
  with members:
    - "improving accuracy and scalability of the pc algorithm by maximizing p value"
    - "scaling up greedy causal search for continuous variables"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "causal inference and causal explanation with background knowledge"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"

Connected through hyperedge HE-2 (induced by source paper "a fast pc algorithm for high dimensional causal discovery with multi core pcs"):
  with members:
    - "from observational studies to causal rule mining"
    - "a parallel algorithm for exact bayesian structure discovery in bayesian networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a hybrid anytime algorithm for the constructiion of causal models from sparse data"

Connected through hyperedge HE-3 (induced by source paper "asp based discovery of semi markovian causal models under weaker assumptions"):
  with members:
    - "ancestral causal inference"
    - "discovering cyclic causal models with latent variables a general sat based procedure"
    - "on the testable implications of causal models with hidden variables"

Connected through hyperedge HE-4 (induced by source paper "comparative benchmarking of causal discovery techniques"):
  with members:
    - "a review on algorithms for constraint based causal discovery"
    - "scaling up greedy equivalence search for continuous variables"
    - "learning sparse causal models is not np hard"
    - "being bayesian about network structure"
    - "causal inference in the presence of latent variables and selection bias"
    - "counterfactual reasoning and learning systems"
    - "searching for bayesian network structures in the space of restricted acyclic partially directed graphs"

Connected through hyperedge HE-5 (induced by source paper "semi parametric network structure discovery models"):
  with members:
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "adam a method for stochastic optimization"
    - "constrained bayesian inference for low rank multitask learning"
    - "invariant gaussian process latent variable models and application in causal discovery"

Connected through hyperedge HE-6 (induced by source paper "a comparison of public causal search packages on linear gaussian data with no latent variables"):
  with members:
    - "causal inference and causal explanation with background knowledge"

Connected through hyperedge HE-7 (induced by source paper "a review on algorithms for constraint based causal discovery"):
  with members:
    - "a fast pc algorithm for high dimensional causal discovery with multi core pcs"
    - "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"
    - "on the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables"

Connected through hyperedge HE-8 (induced by source paper "ancestral causal inference"):
  with members:
    - "clingo asp control preliminary report"
    - "causal discovery from changes"
    - "cyclic causal discovery from continuous equilibrium data"
    - "a logical characterization of constraint based causal discovery"
    - "incorporating causal prior knowledge as path constraints in bayesian networks and maximal ancestral graphs"
    - "a bayesian approach to constraint based causal inference"

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)