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: "learning multi faceted representations of individuals from heterogeneous evidence using neural networks"
  Abstract: Inferring latent attributes of people online is an important social computing task, but requires integrating the many heterogeneous sources of information available on the web. We propose learning individual representations of people using neural nets to integrate rich linguistic and network evidence gathered from social media. The algorithm is able to combine diverse cues, such as the text a person writes, their attributes (e.g. gender, employer, education, location) and social relations to other people. We show that by integrating both textual and network evidence, these representations offer improved performance at four important tasks in social media inference on Twitter: predicting (1) gender, (2) occupation, (3) location, and (4) friendships for users. Our approach scales to large datasets and the learned representations can be used as general features in and have the potential to benefit a large number of downstream tasks including link prediction, community detection, or probabilistic reasoning over social networks.

Connected through hyperedge HE-1 (induced by source paper "learning representations of social media users"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "what s ur type contextualized classification of user types in marijuana related communications using compositional multiview embedding"
    - "multi task learning for argumentation mining in low resource settings"
    - "semi supervised user geolocation via graph convolutional networks"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "deep generalized canonical correlation analysis"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "tweet2vec learning tweet embeddings using character level cnn lstm encoder decoder"

Connected through hyperedge HE-2 (induced by source paper "a tutorial on network embeddings"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "fast warped graph embedding unifying framework and one click algorithm"
    - "sne signed network embedding"
    - "label noise reduction in entity typing by heterogeneous partial label embedding"
    - "node2vec scalable feature learning for networks"
    - "a general framework for content enhanced network representation learning"
    - "a neural network approach to joint modeling social networks and mobile trajectories"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "evidence transfer for improving clustering tasks using external categorical evidence"):
  with members:
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "learning without forgetting"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "joint unsupervised learning of deep representations and image clusters"
    - "a neural algorithm of artistic style"
    - "unsupervised deep embedding for clustering analysis"

Connected through hyperedge HE-4 (induced by source paper "quantifying mental health from social media with neural user embeddings"):
  with members:
    - "modelling context with user embeddings for sarcasm detection in social media"
    - "toward socially infused information extraction embedding authors mentions and entities"
    - "a primer on neural network models for natural language processing"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "modelling context with user embeddings for sarcasm detection in social media"):
  with members:
    - "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "distributed representations of sentences and documents"
    - "notes on noise contrastive estimation and negative sampling"
    - "distributed representations of words and phrases and their compositionality"
    - "adadelta an adaptive learning rate method"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-6 (induced by source paper "toward socially infused information extraction embedding authors mentions and entities"):
  with members:
    - "s mart novel tree based structured learning algorithms applied to tweet entity linking"
    - "overcoming language variation in sentiment analysis with social attention"
    - "line large scale information network embedding"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "distributed representations of words and phrases and their compositionality"
    - "user level sentiment analysis incorporating social networks"

Connected through hyperedge HE-7 (induced by source paper "overcoming language variation in sentiment analysis with social attention"):
  with members:
    - "demographic dialectal variation in social media a case study of african american english"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"
    - "unsupervised domain adaptation with feature embeddings"
    - "exploiting social network structure for person to person sentiment analysis"
    - "a convolutional neural network for modelling sentences"
    - "marginalized denoising autoencoders for domain adaptation"
    - "user level sentiment analysis incorporating social networks"

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)