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: "probabilistic similarity logic"
  Abstract: Many machine learning applications require the ability to learn from and reason about noisy multi-relational data. To address this, several effective representations have been developed that provide both a language for expressing the structural regularities of a domain, and principled support for probabilistic inference. In addition to these two aspects, however, many applications also involve a third aspect-the need to reason about similarities-which has not been directly supported in existing frameworks. This paper introduces probabilistic similarity logic (PSL), a general-purpose framework for joint reasoning about similarity in relational domains that incorporates probabilistic reasoning about similarities and relational structure in a principled way. PSL can integrate any existing domain-specific similarity measures and also supports reasoning about similarities between sets of entities. We provide efficient inference and learning techniques for PSL and demonstrate its effectiveness both in common relational tasks and in settings that require reasoning about similarity.

Connected through hyperedge HE-1 (induced by source paper "non parametric learning of gaifman models"):
  with members:
    - "dual convolutional neural network for graph of graphs link prediction"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "discriminative gaifman models"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"

Connected through hyperedge HE-2 (induced by source paper "neural graph embedding methods for natural language processing"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "confidence based graph convolutional networks for semi supervised learning"
    - "hypernetwork knowledge graph embeddings"
    - "cesi canonicalizing open knowledge bases using embeddings and side information"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "lovasz convolutional networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "graph to sequence learning using gated graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "declarative learning based programming as an interface to ai systems"):
  with members:
    - "tensorlog deep learning meets probabilistic dbs"
    - "dynamic routing between capsules"
    - "a berkeley view of systems challenges for ai"
    - "relational learning and feature extraction by querying over heterogeneous information networks"
    - "programming with a differentiable forth interpreter"
    - "structured factored inference a framework for automated reasoning in probabilistic programming languages"
    - "tensorflow a system for large scale machine learning"
    - "the inductive constraint programming loop"

Connected through hyperedge HE-4 (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"
    - "infinite hidden relational models"

Connected through hyperedge HE-5 (induced by source paper "modeling drug disease relations with linguistic and knowledge graph constraints"):
  with members:
    - "exploiting social network structure for person to person sentiment analysis"
    - "hinge loss markov random fields convex inference for structured prediction"

Connected through hyperedge HE-6 (induced by source paper "efficient knowledge graph accuracy evaluation"):
  with members:
    - "in search of an entity resolution oasis optimal asymptotic sequential importance sampling"

Connected through hyperedge HE-7 (induced by source paper "surface semantically rich fact validation with explanations"):
  with members:
    - "all in one multi task learning for rumour verification"
    - "kg cleaner identifying and correcting errors produced by information extraction systems"
    - "fever a large scale dataset for fact extraction and verification"
    - "a simple but tough to beat baseline for the fake news challenge stance detection task"
    - "automatic detection of fake news"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-8 (induced by source paper "cesi canonicalizing open knowledge bases using embeddings and side information"):
  with members:
    - "relation schema induction using tensor factorization with side information"
    - "holographic embeddings of knowledge graphs"
    - "unsupervised methods for determining object and relation synonyms on the web"

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)