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: "unsupervised methods for determining object and relation synonyms on the web"
  Abstract: The task of identifying synonymous relations and objects, or synonym resolution, is critical for high-quality information extraction. This paper investigates synonym resolution in the context of unsupervised information extraction, where neither hand-tagged training examples nor domain knowledge is available. The paper presents a scalable, fully-implemented system that runs in O(KN log N) time in the number of extractions, N, and the maximum number of synonyms per word, K. The system, called RESOLVER, introduces a probabilistic relational model for predicting whether two strings are co-referential based on the similarity of the assertions containing them. On a set of two million assertions extracted from the Web, RESOLVER resolves objects with 78% precision and 68% recall, and resolves relations with 90% precision and 35% recall. Several variations of RESOLVER's probabilistic model are explored, and experiments demonstrate that under appropriate conditions these variations can improve F1 by 5%. An extension to the basic RESOLVER system allows it to handle polysemous names with 97% precision and 95% recall on a data set from the TREC corpus.

Connected through hyperedge HE-1 (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-2 (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"
    - "probabilistic similarity logic"

Connected through hyperedge HE-3 (induced by source paper "latent relation representations for universal schemas"):
  with members:
    - "bpr bayesian personalized ranking from implicit feedback"

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.CL(Computation and Language)