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 "acquisition of phrase correspondences using natural deduction proofs"):
  Abstract: How to identify, extract, and use phrasal knowledge is a crucial problem for the task of Recognizing Textual Entailment (RTE). To solve this problem, we propose a method for detecting paraphrases via natural deduction proofs of semantic relations between sentence pairs. Our solution relies on a graph reformulation of partial variable unifications and an algorithm that induces subgraph alignments between meaning representations. Experiments show that our method can automatically detect various paraphrases that are absent from existing paraphrase databases. In addition, the detection of paraphrases using proof information improves the accuracy of RTE tasks.

Includes members:
  Member "toward abstractive summarization using semantic representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization")
  Member "a ccg parsing with a supertag and dependency factored model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "head driven phrase structure grammar parsing on penn treebank")
  Member "determining semantic textual similarity using natural deduction proofs":
    also connected through other hyperedges:
      (none)
  Member "task specific attentive pooling of phrase alignments contributes to sentence matching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "monalog a lightweight system for natural language inference based on monotonicity")
  Member "learning semantically and additively compositional distributional representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "locality and compositionality in zero shot learning")
  Member "representing meaning with a combination of logical and distributional models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "eat2seq a generic framework for controlled sentence transformation without task specific training")
  Member "a large annotated corpus for learning natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "contextual lensing of universal sentence representations")

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)