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: "counting belief propagation"
  Abstract: A major benefit of graphical models is that most knowledge is captured in the model structure. Many models, however, produce inference problems with a lot of symmetries not reflected in the graphical structure and hence not exploitable by efficient inference techniques such as belief propagation (BP). In this paper, we present a new and simple BP algorithm, called counting BP, that exploits such additional symmetries. Starting from a given factor graph, counting BP first constructs a compressed factor graph of clusternodes and clusterfactors, corresponding to sets of nodes and factors that are indistinguishable given the evidence. Then it runs a modified BP algorithm on the compressed graph that is equivalent to running BP on the original factor graph. Our experiments show that counting BP is applicable to a variety of important AI tasks such as (dynamic) relational models and boolean model counting, and that significant efficiency gains are obtainable, often by orders of magnitude.

Connected through hyperedge HE-1 (induced by source paper "lifted hybrid variational inference"):
  with members:
    - "coarse to fine lifted map inference in computer vision"
    - "lifted tree reweighted variational inference"
    - "beyond log supermodularity lower bounds and the bethe partition function"
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"
    - "lifted relational variational inference"
    - "lifted inference for relational continuous models"
    - "the bethe partition function of log supermodular graphical models"
    - "nonparametric variational inference"

Connected through hyperedge HE-2 (induced by source paper "generating and sampling orbits for lifted probabilistic inference"):
  with members:
    - "tractability through exchangeability a new perspective on efficient probabilistic inference"
    - "lifted probabilistic inference for asymmetric graphical models"
    - "symmetry aware marginal density estimation"
    - "practical graph isomorphism ii"
    - "automorphism groups of graphical models and lifted variational inference"
    - "markov chains on orbits of permutation groups"
    - "probabilistic theorem proving"

Connected through hyperedge HE-3 (induced by source paper "state space abstractions for probabilistic inference a systematic review"):
  with members:
    - "stochastic planning and lifted inference"
    - "domain recursion for lifted inference with existential quantifiers"
    - "contextual symmetries in probabilistic graphical models"
    - "weighted positive binary decision diagrams for exact probabilistic inference"
    - "lifted probabilistic inference for asymmetric graphical models"
    - "lifted tree reweighted variational inference"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "symmetry aware marginal density estimation"

Connected through hyperedge HE-4 (induced by source paper "block belief propagation for parameter learning in markov random fields"):
  with members:
    - "deeply learning the messages in message passing inference"
    - "very deep convolutional networks for large scale image recognition"
    - "fully convolutional networks for semantic segmentation"
    - "learning graphical model parameters with approximate marginal inference"
    - "efficient decomposed learning for structured prediction"
    - "convexifying the bethe free energy"
    - "a new class of upper bounds on the log partition function"
    - "stochastic belief propagation a low complexity alternative to the sum product algorithm"

Connected through hyperedge HE-5 (induced by source paper "lifted filtering via exchangeable decomposition"):
  with members:
    - "sequential lifted bayesian filtering in multiset rewriting systems"
    - "exchangeable variable models"
    - "tractable inference for complex stochastic processes"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "sum product networks a new deep architecture"
    - "sampling first order logical particles"
    - "probabilistic theorem proving"
    - "policy recognition in the abstract hidden markov model"

Connected through hyperedge HE-6 (induced by source paper "lifted marginal map inference"):
  with members:
    - "solving marginal map problems with np oracles and parity constraints"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "map complexity results and approximation methods"
    - "markov chains on orbits of permutation groups"
    - "lifted graphical models a survey"

Connected through hyperedge HE-7 (induced by source paper "block value symmetries in probabilistic graphical models"):
  with members:
    - "automorphism groups of graphical models and lifted variational inference"

Connected through hyperedge HE-8 (induced by source paper "coarse to fine lifted map inference in computer vision"):
  with members:
    - "learning tractable probabilistic models for fault localization"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "lifted probabilistic inference for asymmetric graphical models"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "rockit exploiting parallelism and symmetry for map inference in statistical relational models"
    - "markov chains on orbits of permutation groups"
    - "lifted graphical models a survey"

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)