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 attribution through function word adjacency networks"
  Abstract: A method for authorship attribution based on function word adjacency networks (WANs) is introduced. Function words are parts of speech that express grammatical relationships between other words but do not carry lexical meaning on their own. In the WANs in this paper, nodes are function words and directed edges from a source function word to a target function word stand in for the likelihood of finding the latter in the ordered vicinity of the former. WANs of different authors can be interpreted as transition probabilities of a Markov chain and are therefore compared in terms of their relative entropies. Optimal selection of WAN parameters is studied and attribution accuracy is benchmarked across a diverse pool of authors and varying text lengths. This analysis shows that, since function words are independent of content, their use tends to be specific to an author and that the relational data captured by function WANs is a good summary of stylometric fingerprints. Attribution accuracy is observed to exceed the one achieved by methods that rely on word frequencies alone. Further combining WANs with methods that rely on word frequencies, results in larger attribution accuracy, indicating that both sources of information encode different aspects of authorial styles.

Connected through hyperedge HE-1 (induced by source paper "edgenets edge varying graph neural networks"):
  with members:
    - "dfnets spectral cnns for graphs with feedback looped filters"
    - "graph neural networks with convolutional arma filters"
    - "stability properties of graph neural networks"
    - "on the transferability of spectral graph filters"
    - "generalizing graph convolutional neural networks with edge variant recursions on graphs"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "attention models in graphs a survey"

Connected through hyperedge HE-2 (induced by source paper "using word embeddings to improve the discriminability of co occurrence text networks"):
  with members:
    - "building networks for image segmentation using particle competition and cooperation"
    - "interactive image segmentation using label propagation through complex networks"
    - "complexity entropy analysis at different levels of organisation in written language"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "linguistic data mining with complex networks a stylometric oriented approach"
    - "word sense disambiguation a complex network approach"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "in narrative texts punctuation marks obey the same statistics as words"

Connected through hyperedge HE-3 (induced by source paper "paragraph based representation of texts a complex networks approach"):
  with members:
    - "an image analysis approach to text analytics based on complex networks"
    - "representation of texts as complex networks a mesoscopic approach"
    - "on the calligraphy of books"
    - "using network science and text analytics to produce surveys in a scientific topic"
    - "using complex networks for text classification discriminating informative and imaginative documents"
    - "topic segmentation via community detection in complex networks"
    - "concentric network symmetry grasps authors styles in word adjacency networks"
    - "a complex network approach to stylometry"

Connected through hyperedge HE-4 (induced by source paper "generalizing graph convolutional neural networks with edge variant recursions on graphs"):
  with members:
    - "graph neural networks with convolutional arma filters"
    - "topology adaptive graph convolutional networks"
    - "median activation functions for graph neural networks"
    - "autoregressive moving average graph filtering"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "convolutional neural networks via node varying graph filters"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"

Connected through hyperedge HE-5 (induced by source paper "stability of graph scattering transforms"):
  with members:
    - "learning decentralized controllers for robot swarms with graph neural networks"
    - "on the transferability of spectral graph filters"
    - "graph convolutional neural networks via scattering"
    - "diffusion scattering transforms on graphs"
    - "geometric deep learning going beyond euclidean data"
    - "network topology inference from spectral templates"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "discrete signal processing on graphs frequency analysis"

Connected through hyperedge HE-6 (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 clustering using multi headed recurrent neural networks"
    - "authorship attribution using a neural network language model"
    - "character level and multi channel convolutional neural networks for large scale authorship attribution"

Connected through hyperedge HE-7 (induced by source paper "a linear complexity multi biometric forensic document analysis system by fusing the stylome and signature modalities"):
  with members:
    - "a complex network approach to stylometry"
    - "on the computational efficiency of training neural networks"
    - "complexity of word collocation networks a preliminary structural analysis"

Connected through hyperedge HE-8 (induced by source paper "online matrix factorization for markovian data and applications to network dictionary learning"):
  with members:
    - "online factorization and partition of complex networks from random walks"

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)