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: "template based inference in symmetric relational markov random fields"
  Abstract: Relational Markov Random Fields are a general and flexible framework for reasoning about the joint distribution over attributes of a large number of interacting entities. The main computational difficulty in learning such models is inference. Even when dealing with complete data, where one can summarize a large domain by sufficient statistics, learning requires one to compute the expectation of the sufficient statistics given different parameter choices. The typical solution to this problem is to resort to approximate inference procedures, such as loopy belief propagation. Although these procedures are quite efficient, they still require computation that is on the order of the number of interactions (or features) in the model. When learning a large relational model over a complex domain, even such approximations require unrealistic running time. In this paper we show that for a particular class of relational MRFs, which have inherent symmetry, we can perform the inference needed for learning procedures using a template-level belief propagation. This procedure's running time is proportional to the size of the relational model rather than the size of the domain. Moreover, we show that this computational procedure is equivalent to sychronous loopy belief propagation. This enables a dramatic speedup in inference and learning time. We use this procedure to learn relational MRFs for capturing the joint distribution of large protein-protein interaction networks.

Connected through hyperedge HE-1 (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-2 (induced by source paper "lifted region based belief propagation"):
  with members:
    - "lifted tree reweighted variational inference"
    - "automorphism groups of graphical models and lifted variational inference"
    - "counting belief propagation"
    - "lifted relax compensate and then recover from approximate to exact lifted probabilistic inference"
    - "probabilistic theorem proving"

Connected through hyperedge HE-3 (induced by source paper "hinge loss markov random fields and probabilistic soft logic"):
  with members:
    - "reinforcement and imitation learning via interactive no regret learning"
    - "exploiting social network structure for person to person sentiment analysis"
    - "inferring user preferences by probabilistic logical reasoning over social networks"
    - "tighter linear program relaxations for high order graphical models"
    - "hinge loss markov random fields convex inference for structured prediction"
    - "a hypergraph partitioned vertex programming approach for large scale consensus optimization"
    - "object oriented bayesian networks"
    - "spook a system for probabilistic object oriented knowledge representation"

Connected through hyperedge HE-4 (induced by source paper "lifted tree reweighted variational inference"):
  with members:
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "automorphism groups of graphical models and lifted variational inference"
    - "tightening lp relaxations for map using message passing"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "convergent propagation algorithms via oriented trees"
    - "a new class of upper bounds on the log partition function"
    - "probabilistic theorem proving"

Connected through hyperedge HE-5 (induced by source paper "contrastive feature induction for efficient structure learning of conditional random fields"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "discriminative probabilistic models for relational data"
    - "a generalized mean field algorithm for variational inference in exponential families"
    - "efficiently inducing features of conditional random fields"

Connected through hyperedge HE-6 (induced by source paper "automorphism groups of graphical models and lifted variational inference"):
  with members:
    - "practical graph isomorphism ii"
    - "markov chains on orbits of permutation groups"
    - "probabilistic theorem proving"

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)