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: "context specific independence in bayesian networks"
  Abstract: Bayesian networks provide a language for qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of the distribution, eases knowledge acquisition, and supports effective inference algorithms. It is well-known, however, that there are certain independencies that we cannot capture qualitatively within the Bayesian network structure: independencies that hold only in certain contexts, i.e., given a specific assignment of values to certain variables. In this paper, we propose a formal notion of context-specific independence (CSI), based on regularities in the conditional probability tables (CPTs) at a node. We present a technique, analogous to (and based on) d-separation, for determining when such independence holds in a given network. We then focus on a particular qualitative representation scheme - tree-structured CPTs - for capturing CSI. We suggest ways in which this representation can be used to support effective inference algorithms. In particular, we present a structural decomposition of the resulting network which can improve the performance of clustering algorithms, and an alternative algorithm based on cutset conditioning.

Connected through hyperedge HE-1 (induced by source paper "abstracting probabilistic models a logical perspective"):
  with members:
    - "state space abstractions for probabilistic inference a systematic review"
    - "explaining transition systems through program induction"
    - "probabilistic program abstractions"
    - "symmetric weighted first order model counting"
    - "inference in probabilistic logic programs using weighted cnf s"
    - "bisimulation based approximate lifted inference"
    - "markov logic in infinite domains"
    - "solving sat and bayesian inference with backtracking search"

Connected through hyperedge HE-2 (induced by source paper "learning distributional programs for relational autocompletion"):
  with members:
    - "learning probabilistic logic programs in continuous domains"
    - "bayes ball the rational pastime for determining irrelevance and requisite information in belief networks and influence diagrams"
    - "discriminative probabilistic models for relational data"
    - "say em for selecting probabilistic models for logical sequences"
    - "lifted inference for relational continuous models"

Connected through hyperedge HE-3 (induced by source paper "factored probabilistic belief tracking"):
  with members:
    - "stochastic simulation algorithms for dynamic probabilistic networks"
    - "tractable inference for complex stochastic processes"
    - "a general non probabilistic theory of inductive reasoning"
    - "the factored frontier algorithm for approximate inference in dbns"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "on the relation between kappa calculus and probabilistic reasoning"
    - "a simple insight into iterative belief propagation s success"

Connected through hyperedge HE-4 (induced by source paper "log linear models independence structure comparison"):
  with members:
    - "blankets joint posterior score for learning markov network structures"
    - "the grow shrink strategy for learning markov network structures constrained by context specific independences"
    - "the ibmap approach for markov network structure learning"
    - "efficiently inducing features of conditional random fields"

Connected through hyperedge HE-5 (induced by source paper "sum product network decompilation"):
  with members:
    - "probabilistic deep learning using random sum product networks"
    - "on relaxing determinism in arithmetic circuits"
    - "on the relationship between sum product networks and bayesian networks"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-6 (induced by source paper "approximate causal abstraction"):
  with members:
    - "abstracting causal models"
    - "searching for bayesian network structures in the space of restricted acyclic partially directed graphs"

Connected through hyperedge HE-7 (induced by source paper "causal decision trees"):
  with members:
    - "explanation trees for causal bayesian networks"

Connected through hyperedge HE-8 (induced by source paper "exploiting causality for selective belief filtering in dynamic bayesian networks"):
  with members:
    - "vector space analysis of belief state approximation for pomdps"
    - "causal independence for knowledge acquisition and inference"
    - "tractable inference for complex stochastic processes"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"
    - "d separation from theorems to algorithms"
    - "the factored frontier algorithm for approximate inference in dbns"
    - "value directed belief state approximation for pomdps"

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)