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 new look at causal independence"
  Abstract: Heckerman (1993) defined causal independence in terms of a set of temporal conditional independence statements. These statements formalized certain types of causal interaction where (1) the effect is independent of the order that causes are introduced and (2) the impact of a single cause on the effect does not depend on what other causes have previously been applied. In this paper, we introduce an equivalent a temporal characterization of causal independence based on a functional representation of the relationship between causes and the effect. In this representation, the interaction between causes and effect can be written as a nested decomposition of functions. Causal independence can be exploited by representing this decomposition in the belief network, resulting in representations that are more efficient for inference than general causal models. We present empirical results showing the benefits of a causal-independence representation for belief-network inference.

Connected through hyperedge HE-1 (induced by source paper "uno uncertainty aware noisy or multimodal fusion for unanticipated input degradation"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "on single source robustness in deep fusion models"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "the fishyscapes benchmark measuring blind spots in semantic segmentation"
    - "benchmarking robustness in object detection autonomous driving when winter is coming"
    - "rfbnet deep multimodal networks with residual fusion blocks for rgb d semantic segmentation"
    - "evaluating bayesian deep learning methods for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "on discrimination discovery and removal in ranked data using causal graph"):
  with members:
    - "achieving non discrimination in prediction"
    - "fa ir a fair top k ranking algorithm"
    - "exposing the probabilistic causal structure of discrimination"
    - "achieving non discrimination in data release"
    - "measuring fairness in ranked outputs"
    - "a causal framework for discovering and removing direct and indirect discrimination"
    - "fairness through awareness"

Connected through hyperedge HE-3 (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 generalization of the noisy or model"
    - "context specific independence in bayesian networks"
    - "d separation from theorems to algorithms"
    - "the factored frontier algorithm for approximate inference in dbns"
    - "value directed belief state approximation for pomdps"

Connected through hyperedge HE-4 (induced by source paper "a causal framework for discovering and removing direct and indirect discrimination"):
  with members:
    - "exposing the probabilistic causal structure of discrimination"

Connected through hyperedge HE-5 (induced by source paper "relational approach to knowledge engineering for pomdp based assistance systems as a translation of a psychological model"):
  with members:
    - "object oriented bayesian networks"
    - "spudd stochastic planning using decision diagrams"
    - "probabilistic theorem proving"

Connected through hyperedge HE-6 (induced by source paper "independence of causal influence and clique tree propagation"):
  with members:
    - "search based methods to bound diagnostic probabilities in very large belief nets"
    - "utility based abstraction and categorization"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "causal independence for knowledge acquisition and inference"
    - "the use of conflicts in searching bayesian networks"
    - "a generalization of the noisy or model"
    - "abstraction in belief networks the role of intermediate states in diagnostic reasoning"
    - "context specific independence in bayesian networks"

Connected through hyperedge HE-7 (induced by source paper "context specific independence in bayesian networks"):
  with members:
    - "approximations in bayesian belief universe for knowledge based systems"
    - "causal independence for knowledge acquisition and inference"
    - "on heuristics for finding loop cutsets in multiply connected belief networks"
    - "learning bayesian networks with local structure"
    - "a probabilistic calculus of actions"
    - "advances in probabilistic reasoning"
    - "a generalization of the noisy or model"

Connected through hyperedge HE-8 (induced by source paper "sample and accumulate algorithms for belief updating in bayes networks"):
  with members:
    - "some properties of joint probability distributions"
    - "the use of conflicts in searching bayesian networks"
    - "a stratified simulation scheme for inference in bayesian belief networks"
    - "backward simulation in bayesian networks"
    - "state space abstraction for anytime evaluation of probabilistic networks"
    - "incremental probabilistic 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)