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: "deepcity a feature learning framework for mining location check ins"
  Abstract: Online social networks being extended to geographical space has resulted in large amount of user check-in data. Understanding check-ins can help to build appealing applications, such as location recommendation. In this paper, we propose DeepCity, a feature learning framework based on deep learning, to profile users and locations, with respect to user demographic and location category prediction. Both of the predictions are essential for social network companies to increase user engagement. The key contribution of DeepCity is the proposal of task-specific random walk which uses the location and user properties to guide the feature learning to be specific to each prediction task. Experiments conducted on 42M check-ins in three cities collected from Instagram have shown that DeepCity achieves a superior performance and outperforms other baseline models significantly.

Connected through hyperedge HE-1 (induced by source paper "exploiting unintended feature leakage in collaborative learning"):
  with members:
    - "towards demystifying membership inference attacks"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "understanding membership inferences on well generalized learning models"
    - "stealing hyperparameters in machine learning"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "knock knock who s there membership inference on aggregate location data"
    - "differentially private federated learning a client level perspective"
    - "logan membership inference attacks against generative models"

Connected through hyperedge HE-2 (induced by source paper "deepmove learning place representations through large scale movement data"):
  with members:
    - "probabilistic typology deep generative models of vowel inventories"
    - "the interpretation of dream meaning resolving ambiguity using latent semantic analysis in a small corpus of text"
    - "node2vec scalable feature learning for networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "neural architectures for named entity recognition"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-3 (induced by source paper "tagvisor a privacy advisor for sharing hashtags"):
  with members:
    - "knock knock who s there membership inference on aggregate location data"
    - "what does the crowd say about you evaluating aggregation based location privacy"
    - "walk2friends inferring social links from mobility profiles"
    - "back to the drawing board revisiting the design of optimal location privacy preserving mechanisms"
    - "fetishizing food in digital age foodporn around the world"
    - "quantifying location sociality"
    - "dawn of the selfie era the whos wheres and hows of selfies on instagram"
    - "deep learning for answer sentence selection"

Connected through hyperedge HE-4 (induced by source paper "ml leaks model and data independent membership inference attacks and defenses on machine learning models"):
  with members:
    - "tagvisor a privacy advisor for sharing hashtags"
    - "chiron privacy preserving machine learning as a service"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "walk2friends inferring social links from mobility profiles"
    - "adversarial image perturbation for privacy protection a game theory perspective"
    - "machine learning models that remember too much"
    - "stealing machine learning models via prediction apis"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-5 (induced by source paper "learning cheap and novel flight itineraries"):
  with members:
    - "deep embedding forest forest based serving with deep embedding features"
    - "customer lifetime value prediction using embeddings"
    - "related pins at pinterest the evolution of a real world recommender system"
    - "entity embeddings of categorical variables"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "walk2friends inferring social links from mobility profiles"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "quantifying location sociality"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "location prediction communities speak louder than friends"
    - "analyzing user activities demographics social network structure and user generated content on instagram"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

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)