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: "rockit exploiting parallelism and symmetry for map inference in statistical relational models"
  Abstract: RockIt is a maximum a-posteriori (MAP) query engine for statistical relational models. MAP inference in graphical models is an optimization problem which can be compiled to integer linear programs (ILPs). We describe several advances in translating MAP queries to ILP instances and present the novel meta-algorithm cutting plane aggregation (CPA). CPA exploits local context-specific symmetries and bundles up sets of linear constraints. The resulting counting constraints lead to more compact ILPs and make the symmetry of the ground model more explicit to state-of-the-art ILP solvers. Moreover, RockIt parallelizes most parts of the MAP inference pipeline taking advantage of ubiquitous shared-memory multi-core architectures. #R##N#We report on extensive experiments with Markov logic network (MLN) benchmarks showing that RockIt outperforms the state-of-the-art systems Alchemy, Markov TheBeast, and Tuffy both in terms of efficiency and quality of results.

Connected through hyperedge HE-1 (induced by source paper "beyond markov logic efficient mining of prediction rules in large graphs"):
  with members:
    - "knowledge base completion baselines strike back"
    - "analogical inference for multi relational embeddings"
    - "complex embeddings for simple link prediction"
    - "holographic embeddings of knowledge graphs"
    - "improving the accuracy and efficiency of map inference for markov logic"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"

Connected through hyperedge HE-2 (induced by source paper "finding minimal cost herbrand models with branch cut and price"):
  with members:
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"

Connected through hyperedge HE-3 (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"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "lifted graphical models a survey"

Connected through hyperedge HE-4 (induced by source paper "mandolin a knowledge discovery framework for the web of data"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "holographic embeddings of knowledge graphs"
    - "deepwalk online learning of social representations"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"

Connected through hyperedge HE-5 (induced by source paper "contextual symmetries in probabilistic graphical models"):
  with members:
    - "graph isomorphism in quasipolynomial time"
    - "lifted probabilistic inference for asymmetric graphical models"
    - "automorphism groups of graphical models and lifted variational inference"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "lifted graphical models a survey"

Connected through hyperedge HE-6 (induced by source paper "ontology matching with knowledge rules"):
  with members:
    - "a markov model for ontology alignment"
    - "improving the accuracy and efficiency of map inference for markov logic"

Connected through hyperedge HE-7 (induced by source paper "tractability through exchangeability a new perspective on efficient probabilistic inference"):
  with members:
    - "exchangeable variable models"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "context specific independence in bayesian networks"
    - "symmetry aware marginal density estimation"
    - "automorphism groups of graphical models and lifted variational inference"
    - "markov chains on orbits of permutation groups"
    - "probabilistic theorem proving"

Connected through hyperedge HE-8 (induced by source paper "lifted probabilistic inference for asymmetric graphical models"):
  with members:
    - "tractability through exchangeability a new perspective on efficient probabilistic inference"
    - "understanding the complexity of lifted inference and asymmetric weighted model counting"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "symmetry aware marginal density estimation"
    - "automorphism groups of graphical models and lifted variational inference"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"

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)