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 knowledge compilation map"
  Abstract: We propose a perspective on knowledge compilation which calls for analyzing different compilation approaches according to two key dimensions: the succinctness of the target compilation language, and the class of queries and transformations that the language supports in polytime. We then provide a knowledge compilation map, which analyzes a large number of existing target compilation languages according to their succinctness and their polytime transformations and queries. We argue that such analysis is necessary for placing new compilation approaches within the context of existing ones. We also go beyond classical, flat target compilation languages based on CNF and DNF, and consider a richer, nested class based on directed acyclic graphs (such as OBDDs), which we show to include a relatively large number of target compilation languages.

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"
    - "context specific independence in bayesian networks"
    - "inference in probabilistic logic programs using weighted cnf s"
    - "bisimulation based approximate lifted inference"
    - "markov logic in infinite domains"

Connected through hyperedge HE-2 (induced by source paper "on the reasons behind decisions"):
  with members:
    - "on validating repairing and refining heuristic ml explanations"
    - "a symbolic approach to explaining bayesian network classifiers"
    - "why should i trust you explaining the predictions of any classifier"
    - "the language of search"
    - "propositional independence formula variable independence and forgetting"

Connected through hyperedge HE-3 (induced by source paper "bounds on the size of pc and urc formulas"):
  with members:
    - "on tackling the limits of resolution in sat solving"
    - "circuit complexity and decompositions of global constraints"

Connected through hyperedge HE-4 (induced by source paper "embedding symbolic knowledge into deep networks"):
  with members:
    - "learning logistic circuits"
    - "hyperprior induced unsupervised disentanglement of latent representations"
    - "can neural networks understand logical entailment"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "a survey on deep learning in medical image analysis"
    - "logic tensor networks for semantic image interpretation"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "harnessing deep neural networks with logic rules"

Connected through hyperedge HE-5 (induced by source paper "neural probabilistic logic programming in deepproblog"):
  with members:
    - "neural logic machines"
    - "playgol learning programs through play"
    - "nlprolog reasoning with weak unification for question answering in natural language"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "learning relational representations with auto encoding logic programs"
    - "deepproblog neural probabilistic logic programming"
    - "neural guided constraint logic programming for program synthesis"
    - "learning explanatory rules from noisy data"

Connected through hyperedge HE-6 (induced by source paper "learning to reason leveraging neural networks for approximate dnf counting"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "learning to solve np complete problems a graph neural network for decision tsp"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "approximate knowledge compilation by online collapsed importance sampling"
    - "how powerful are graph neural networks"
    - "inference in probabilistic graphical models by graph neural networks"
    - "on hashing based approaches to approximate dnf counting"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-7 (induced by source paper "on tractable computation of expected predictions"):
  with members:
    - "visualizing and understanding sum product networks"
    - "smoothing structured decomposable circuits"
    - "what to expect of classifiers reasoning about logistic regression with missing features"
    - "learning logistic circuits"
    - "metabags bagged meta decision trees for regression"
    - "explaining image classifiers by counterfactual generation"
    - "a unified approach to interpreting model predictions"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-8 (induced by source paper "semantically regularized logic graph embeddings"):
  with members:
    - "hyperprior induced unsupervised disentanglement of latent representations"
    - "can neural networks understand logical entailment"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "a survey on deep learning in medical image analysis"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "harnessing deep neural networks with logic rules"
    - "learning continuous semantic representations of symbolic expressions"
    - "label free supervision of neural networks with physics and domain knowledge"

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)