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: "twitter user geolocation using deep multiview learning"
  Abstract: Predicting the geographical location of users on social networks like Twitter is an active research topic with plenty of methods proposed so far. Most of the existing work follows either a content-based or a network-based approach. The former is based on user-generated content while the latter exploits the structure of the network of users. In this paper, we propose a more generic approach, which incorporates not only both content-based and network-based features, but also other available information into a unified model. Our approach, named Multi-Entry Neural Network (MENET), leverages the latest advances in deep learning and multiview learning. A realization of MENET with textual, network and metadata features results in an effective method for Twitter user geolocation, achieving the state of the art on two well-known datasets.

Connected through hyperedge HE-1 (induced by source paper "dianet bert and hierarchical attention multi task learning of fine grained dialect"):
  with members:
    - "automatic language identification in texts a survey"
    - "arap tweet a large multi dialect twitter corpus for gender age and language variety identification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "semi supervised multitask learning for sequence labeling"
    - "recurrent neural network for text classification with multi task learning"
    - "multi task sequence to sequence learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "social media based user embedding a literature review"):
  with members:
    - "attributed social network embedding"
    - "quantifying mental health from social media with neural user embeddings"
    - "what the language you tweet says about your occupation"
    - "node2vec scalable feature learning for networks"
    - "modelling context with user embeddings for sarcasm detection in social media"
    - "deepwalk online learning of social representations"
    - "very deep convolutional networks for large scale image recognition"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-3 (induced by source paper "regularizing autoencoder based matrix completion models via manifold learning"):
  with members:
    - "deep learning sparse ternary projections for compressed sensing of images"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "an overview of multi task learning in deep neural networks"
    - "hybrid collaborative filtering with neural networks"
    - "hybrid collaborative filtering with autoencoders"
    - "a neural autoregressive approach to collaborative filtering"
    - "hybrid recommender system based on autoencoders"
    - "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)