human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "definition frames using definitions for hybrid concept representations"):
  Abstract: Concept representations is a particularly active area in NLP. Although recent advances in distributional semantics have shown tremendous improvements in performance, they still lack semantic interpretability. In this paper, we introduce a novel hybrid representation called Definition Frames, which is extracted from definitions under the formulation of domain-transfer Relation Extraction. Definition Frames are easily reformulated to a matrix representation where each row is semantically meaningful. This results in a fluid representation, where we can prune dimension(s) according to the type of information we want to retain for any specific task. Our results show that Definition Frames (1) maintain the significant semantic information of the original definition (human evaluation) and (2) have competitive performance with other distributional semantic approaches on word similarity tasks. Furthermore, our experiments show substantial improvements over word-embeddings when fine-tuned to a task even using only a linear transform.

Includes members:
  Member "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning from explanations with neural execution tree")
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "definition modeling learning to define word embeddings in natural language":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probing for semantic classes diagnosing the meaning content of word embeddings")
  Member "end to end sequence labeling via bi directional lstm cnns crf":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "enriching word vectors with subword information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "counter fitting word vectors to linguistic constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "word embedding algorithms as generalized low rank models and their canonical form")
  Member "problems with evaluation of word embeddings using word similarity tasks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")

Question: Please tell me which class the source paper 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)