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 "a machine learning framework for authorship identification from texts"):
  Abstract: Authorship identification is a process in which the author of a text is identified. Most known literary texts can easily be attributed to a certain author because they are, for example, signed. Yet sometimes we find unfinished pieces of work or a whole bunch of manuscripts with a wide variety of possible authors. In order to assess the importance of such a manuscript, it is vital to know who wrote it. In this work, we aim to develop a machine learning framework to effectively determine authorship. We formulate the task as a single-label multi-class text categorization problem and propose a supervised machine learning framework incorporating stylometric features. This task is highly interdisciplinary in that it takes advantage of machine learning, information retrieval, and natural language processing. We present an approach and a model which learns the differences in writing style between $50$ different authors and is able to predict the author of a new text with high accuracy. The accuracy is seen to increase significantly after introducing certain linguistic stylometric features along with text features.

Includes members:
  Member "an unsupervised domain independent framework for automated detection of persuasion tactics in text":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transition based dependency parsing using perceptron learner")
  Member "a heterogeneous graphical model to understand user level sentiments in social media":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transition based dependency parsing using perceptron learner")
  Member "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transition based dependency parsing using perceptron learner")
  Member "object sensitive deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self supervised object level deep reinforcement learning")
  Member "transparency and explanation in deep reinforcement learning neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exploratory not explanatory counterfactual analysis of saliency maps for deep reinforcement learning")
  Member "photorealistic style transfer for videos":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transition based dependency parsing using perceptron learner")
  Member "joint embedding of hierarchical categories and entities for concept categorization and dataless classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "wikipedia2vec an efficient toolkit for learning and visualizing the embeddings of words and entities from wikipedia")
  Member "content based video indexing and retrieval using corr lda":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transition based dependency parsing using perceptron learner")

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)