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: "improving the accuracy and efficiency of map inference for markov logic"
  Abstract: In this work we present Cutting Plane Inference (CPI), a Maximum A Posteriori (MAP) inference method for Statistical Relational Learning. Framed in terms of Markov Logic and inspired by the Cutting Plane Method, it can be seen as a meta algorithm that instantiates small parts of a large and complex Markov Network and then solves these using a conventional MAP method. We evaluate CPI on two tasks, Semantic Role Labelling and Joint Entity Resolution, while plugging in two different MAP inference methods: the current method of choice for MAP inference in Markov Logic, MaxWalkSAT, and Integer Linear Programming. We observe that when used with CPI both methods are significantly faster than when used alone. In addition, CPI improves the accuracy of MaxWalkSAT and maintains the exactness of Integer Linear Programming.

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"
    - "rockit exploiting parallelism and symmetry for map inference in statistical relational models"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"

Connected through hyperedge HE-2 (induced by source paper "xor sampling for network design with correlated stochastic events"):
  with members:
    - "solving marginal map problems with np oracles and parity constraints"
    - "a scalable and nearly uniform generator of sat witnesses"
    - "maximizing the spread of cascades using network design"

Connected through hyperedge HE-3 (induced by source paper "towards log linear logics with concrete domains"):
  with members:
    - "heuristic ranking in tightly coupled probabilistic description logics"
    - "description logics with fuzzy concrete domains"

Connected through hyperedge HE-4 (induced by source paper "ontology matching with knowledge rules"):
  with members:
    - "a markov model for ontology alignment"
    - "rockit exploiting parallelism and symmetry for map inference in statistical relational models"

Connected through hyperedge HE-5 (induced by source paper "rockit exploiting parallelism and symmetry for map inference in statistical relational models"):
  with members:
    - "automorphism groups of graphical models and lifted variational inference"
    - "a delayed column generation strategy for exact k bounded map inference in markov logic networks"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "exploiting uniform assignments in first order mpe"
    - "probabilistic theorem proving"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"
    - "graphlab a new framework for parallel machine learning"

Connected through hyperedge HE-6 (induced by source paper "taming the curse of dimensionality discrete integration by hashing and optimization"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "a scalable and nearly uniform generator of sat witnesses"
    - "bayesian learning in undirected graphical models approximate mcmc algorithms"
    - "map complexity results and approximation methods"
    - "on the partition function and random maximum a posteriori perturbations"
    - "tightening lp relaxations for map using message passing"
    - "a decidable dichotomy theorem on directed graph homomorphisms with non negative weights"

Connected through hyperedge HE-7 (induced by source paper "integrative semantic dependency parsing via efficient large scale feature selection"):
  with members:
    - "combination strategies for semantic role labeling"

Connected through hyperedge HE-8 (induced by source paper "probabilistic event calculus for event recognition"):
  with members:
    - "a probabilistic logic programming event calculus"
    - "location based reasoning about complex multi agent behavior"
    - "probabilistic reasoning about actions in nonmonotonic causal theories"
    - "sampling first order logical particles"
    - "counting belief propagation"
    - "on the implementation of the probabilistic logic programming language problog"
    - "a logic programming approach to activity recognition"
    - "logical hidden markov models"

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)