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: "cardinal virtues extracting relation cardinalities from text"
  Abstract: Information extraction (IE) from text has largely focused on relations between individual entities, such as who has won which award. However, some facts are never fully mentioned, and no IE method has perfect recall. Thus, it is beneficial to also tap contents about the cardinalities of these relations, for example, how many awards someone has won. We introduce this novel problem of extracting cardinalities and discusses the specific challenges that set it apart from standard IE. We present a distant supervision method using conditional random fields. A preliminary evaluation results in precision between 3% and 55%, depending on the difficulty of relations.

Connected through hyperedge HE-1 (induced by source paper "uncovering hidden semantics of set information in knowledge bases"):
  with members:
    - "paris probabilistic alignment of relations instances and schema"
    - "conjunctive query answering for the description logic shiq"

Connected through hyperedge HE-2 (induced by source paper "embedding cardinality constraints in neural link predictors"):
  with members:
    - "improving knowledge graph embedding using simple constraints"
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "predicting completeness in knowledge bases"
    - "adversarial sets for regularising neural link predictors"
    - "poincar e embeddings for learning hierarchical representations"
    - "complex embeddings for simple link prediction"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "enriching knowledge bases with counting quantifiers"):
  with members:
    - "neural architectures for named entity recognition"
    - "from data fusion to knowledge fusion"
    - "adam a method for stochastic optimization"

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)