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: "multi level representations for fine grained typing of knowledge base entities"
  Abstract: Entities are essential elements of natural language. In this paper, we present methods for learning multi-level representations of entities on three complementary levels: character (character patterns in entity names extracted, e.g., by neural networks), word (embeddings of words in entity names) and entity (entity embeddings). We investigate state-of-the-art learning methods on each level and find large differences, e.g., for deep learning models, traditional ngram features and the subword model of fasttext (Bojanowski et al., 2016) on the character level; for word2vec (Mikolov et al., 2013) on the word level; and for the order-aware model wang2vec (Ling et al., 2015a) on the entity level. We confirm experimentally that each level of representation contributes complementary information and a joint representation of all three levels improves the existing embedding based baseline for fine-grained entity typing by a large margin. Additionally, we show that adding information from entity descriptions further improves multi-level representations of entities.

Connected through hyperedge HE-1 (induced by source paper "ernie enhanced language representation with informative entities"):
  with members:
    - "ernie enhanced representation through knowledge integration"
    - "improving question answering by commonsense based pre training"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "joint representation learning of cross lingual words and entities via attentive distant supervision"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "ultra fine entity typing"

Connected through hyperedge HE-2 (induced by source paper "learning to denoise distantly labeled data for entity typing"):
  with members:
    - "imposing label relational inductive bias for extremely fine grained entity typing"
    - "ultra fine entity typing"
    - "deep contextualized word representations"
    - "deep probabilistic logic a unifying framework for indirect supervision"
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "end to end neural coreference resolution"
    - "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix"
    - "snorkel rapid training data creation with weak supervision"

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)