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: "ceres distantly supervised relation extraction from the semi structured web"
  Abstract: The web contains countless semi-structured websites, which can be a rich source of information for populating knowledge bases. Existing methods for extracting relations from the DOM trees of semi-structured webpages can achieve high precision and recall only when manual annotations for each website are available. Although there have been efforts to learn extractors from automatically-generated labels, these methods are not sufficiently robust to succeed in settings with complex schemas and information-rich websites. #R##N#In this paper we present a new method for automatic extraction from semi-structured websites based on distant supervision. We automatically generate training labels by aligning an existing knowledge base with a web page and leveraging the unique structural characteristics of semi-structured websites. We then train a classifier based on the potentially noisy and incomplete labels to predict new relation instances. Our method can compete with annotation-based techniques in the literature in terms of extraction quality. A large-scale experiment on over 400,000 pages from dozens of multi-lingual long-tail websites harvested 1.25 million facts at a precision of 90%.

Connected through hyperedge HE-1 (induced by source paper "knowledge graphs"):
  with members:
    - "modl a modular ontology design library"
    - "drum end to end differentiable rule mining on knowledge graphs"
    - "hypernetwork knowledge graph embeddings"
    - "a tutorial on modular ontology modeling with ontology design patterns the cooking recipes ontology"
    - "building an ecosystem for the tyrolean tourism knowledge graph"
    - "eventkg a multilingual event centric temporal knowledge graph"
    - "how powerful are graph neural networks"
    - "knowledge graph embedding with iterative guidance from soft rules"

Connected through hyperedge HE-2 (induced by source paper "open knowledge enrichment for long tail entities"):
  with members:
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "truth inference at scale a bayesian model for adjudicating highly redundant crowd annotations"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "open world knowledge graph completion"
    - "convolutional 2d knowledge graph embeddings"
    - "document filtering for long tail entities"

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)