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 "composite semantic relation classification"):
  Abstract: Different semantic interpretation tasks such as text entailment and question answering require the classification of semantic relations between terms or entities within text. However, in most cases it is not possible to assign a direct semantic relation between entities/terms. This paper proposes an approach for composite semantic relation classification, extending the traditional semantic relation classification task. Different from existing approaches, which use machine learning models built over lexical and distributional word vector features, the proposed model uses the combination of a large commonsense knowledge base of binary relations, a distributional navigational algorithm and sequence classification to provide a solution for the composite semantic relation classification problem.

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 "improved relation classification by deep recurrent neural networks with data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reflex flexible framework for relation extraction in multiple domains")
  Member "combining neural networks and log linear models to improve relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relation adversarial network for low resource knowledgegraph completion")
  Member "dinfra a one stop shop for computing multilingual semantic relatedness":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semantic relatedness for all languages a comparative analysis of multilingual semantic relatedness using machine translation")
  Member "classifying relations by ranking with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "classifying relations via long short term memory networks along shortest dependency path":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reflex flexible framework for relation extraction in multiple domains")
  Member "semantic relation classification via convolutional neural networks with simple negative sampling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "graph representation learning a survey")
  Member "a dependency based neural network for relation classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "leveraging dependency forest for neural medical relation extraction")

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)