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: "infinite hidden relational models"
  Abstract: In many cases it makes sense to model a relationship symmetrically, not implying any particular directionality. Consider the classical example of a recommendation system where the rating of an item by a user should symmetrically be dependent on the attributes of both the user and the item. The attributes of the (known) relationships are also relevant for predicting attributes of entities and for predicting attributes of new relations. In recommendation systems, the exploitation of relational attributes is often referred to as collaborative filtering. Again, in many applications one might prefer to model the collaborative effect in a symmetrical way. In this paper we present a relational model, which is completely symmetrical. The key innovation is that we introduce for each entity (or object) an infinite-dimensional latent variable as part of a Dirichlet process (DP) model. We discuss inference in the model, which is based on a DP Gibbs sampler, i.e., the Chinese restaurant process. We extend the Chinese restaurant process to be applicable to relational modeling. Our approach is evaluated in three applications. One is a recommendation system based on the MovieLens data set. The second application concerns the prediction of the function of yeast genes/proteins on the data set of KDD Cup 2001 using a multi-relational model. The third application involves a relational medical domain. The experimental results show that our model gives significantly improved estimates of attributes describing relationships or entities in complex relational models.

Connected through hyperedge HE-1 (induced by source paper "heterogeneous relational reasoning in knowledge graphs with reinforcement learning"):
  with members:
    - "graph few shot learning via knowledge transfer"
    - "reinforcement knowledge graph reasoning for explainable recommendation"
    - "reinforcewalk learning to walk in graph with monte carlo tree search"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "top k off policy correction for a reinforce recommender system"
    - "deep reinforcement learning based recommendation with explicit user item interactions modeling"
    - "one shot relational learning for knowledge graphs"
    - "m walk learning to walk over graphs using monte carlo tree search"

Connected through hyperedge HE-2 (induced by source paper "logic rules powered knowledge graph embedding"):
  with members:
    - "paragraphe a library for parallel knowledge graph embedding"
    - "harnessing deep neural networks with logic rules"
    - "modeling relation paths for representation learning of knowledge bases"
    - "holographic embeddings of knowledge graphs"
    - "connecting language and knowledge bases with embedding models for relation extraction"
    - "probabilistic similarity logic"

Connected through hyperedge HE-3 (induced by source paper "holographic embeddings of knowledge graphs"):
  with members:
    - "traversing knowledge graphs in vector space"
    - "knowledge based trust estimating the trustworthiness of web sources"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-4 (induced by source paper "an agent driven semantical identifier using radial basis neural networks and reinforcement learning"):
  with members:
    - "a cascade neural network architecture investigating surface plasmon polaritons propagation for thin metals in openmp"
    - "simplified firefly algorithm for 2d image key points search"
    - "improving files availability for bittorrent using a diffusion model"
    - "using modularity metrics to assist move method refactoring of large systems"
    - "a hybrid neurowavelet predictor for qos control and stability"

Connected through hyperedge HE-5 (induced by source paper "compiling relational database schemata into probabilistic graphical models"):
  with members:
    - "discriminative probabilistic models for relational data"
    - "church a language for generative 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)