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: "authorship clustering using multi headed recurrent neural networks"
  Abstract: A recurrent neural network that has been trained to separately model the language of several documents by unknown authors is used to measure similarity between the documents. It is able to find clues of common authorship even when the documents are very short and about disparate topics. While it is easy to make statistically significant predictions regarding authorship, it is difficult to group documents into definite clusters with high accuracy.

Connected through hyperedge HE-1 (induced by source paper "style aware neural model with application in authorship attribution"):
  with members:
    - "experiments with neural networks for small and large scale authorship verification"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "deep rnns encode soft hierarchical syntax"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "the effect of different writing tasks on linguistic style a case study of the roc story cloze task"
    - "authorship attribution using a neural network language model"
    - "character level and multi channel convolutional neural networks for large scale authorship attribution"
    - "authorship attribution through function word adjacency networks"

Connected through hyperedge HE-2 (induced by source paper "syntactic recurrent neural network for authorship attribution"):
  with members:
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "the effect of different writing tasks on linguistic style a case study of the roc story cloze task"
    - "authorship attribution using a neural network language model"
    - "effective use of word order for text categorization with convolutional neural networks"
    - "convolutional neural networks for sentence classification"
    - "natural language processing almost from scratch"

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)