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: "complexity of word collocation networks a preliminary structural analysis"
  Abstract: In this paper, we explore complex network properties of word collocation networks (Ferret, 2002) from four different genres. Each document of a particular genre was converted into a network of words with word collocations as edges. We analyzed graphically and statistically how the global properties of these networks varied across different genres, and among different network types within the same genre. Our results indicate that the distributions of network properties are visually similar but statistically apart across different genres, and interesting variations emerge when we consider different network types within a single genre. We further investigate how the global properties change as we add more and more collocation edges to the graph of one particular genre, and observe that except for the number of vertices and the size of the largest connected component, network properties change in phases, via jumps and drops.

Connected through hyperedge HE-1 (induced by source paper "a neural grammatical error correction system built on better pre training and sequential transfer learning"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "corpora generation for grammatical error correction"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "weakly supervised grammatical error correction using iterative decoding"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "reaching human level performance in automatic grammatical error correction an empirical study"

Connected through hyperedge HE-2 (induced by source paper "adapting language models for non parallel author stylized rewriting"):
  with members:
    - "a lexical syntactic and semantic perspective for understanding style in text"
    - "mass masked sequence to sequence pre training for language generation"
    - "evaluating style transfer for text"
    - "cross lingual language model pretraining"
    - "multiple attribute text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "polite dialogue generation without parallel data"

Connected through hyperedge HE-3 (induced by source paper "a lexical syntactic and semantic perspective for understanding style in text"):
  with members:
    - "style transformer unpaired text style transfer without disentangled latent representation"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "polite dialogue generation without parallel data"
    - "a multi task ensemble framework for emotion sentiment and intensity prediction"
    - "style transfer from non parallel text by cross alignment"
    - "wassa 2017 shared task on emotion intensity"
    - "style transfer in text exploration and evaluation"

Connected through hyperedge HE-4 (induced by source paper "visualization of very large high dimensional data sets as minimum spanning trees"):
  with members:
    - "moleculenet a benchmark for molecular machine learning"
    - "improved consistent weighted sampling revisited"
    - "exploration and visualization in the web of big linked data a survey of the state of the art"
    - "hashing for similarity search a survey"

Connected through hyperedge HE-5 (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"
    - "authorship attribution through function word adjacency networks"
    - "on the computational efficiency of training neural networks"

Connected through hyperedge HE-6 (induced by source paper "manipulating the difficulty of c tests"):
  with members:
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"

Connected through hyperedge HE-7 (induced by source paper "unbounded cache model for online language modeling with open vocabulary"):
  with members:
    - "fasttext zip compressing text classification models"
    - "efficient softmax approximation for gpus"
    - "recurrent highway networks"
    - "improving neural language models with a continuous cache"
    - "exploring the limits of language modeling"
    - "pointer sentinel mixture models"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-8 (induced by source paper "machine learning models that remember too much"):
  with members:
    - "sok security and privacy in machine learning"
    - "understanding deep learning requires rethinking generalization"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "membership inference attacks against machine learning models"
    - "theano a python framework for fast computation of mathematical expressions"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "neural networks with few multiplications"
    - "deep residual learning for image recognition"

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.SI(Social and Information Networks)