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: "you are who you know and how you behave attribute inference attacks via users social friends and behaviors"
  Abstract: We propose new privacy attacks to infer attributes (e.g., locations, occupations, and interests) of online social network users. Our attacks leverage seemingly innocent user information that is publicly available in online social networks to infer missing attributes of targeted users. Given the increasing availability of (seemingly innocent) user information online, our results have serious implications for Internet privacy -- private attributes can be inferred from users' publicly available data unless we take steps to protect users from such inference attacks. #R##N#To infer attributes of a targeted user, existing inference attacks leverage either the user's publicly available social friends or the user's behavioral records (e.g., the webpages that the user has liked on Facebook, the apps that the user has reviewed on Google Play), but not both. As we will show, such inference attacks achieve limited success rates. However, the problem becomes qualitatively different if we consider both social friends and behavioral records. To address this challenge, we develop a novel model to integrate social friends and behavioral records and design new attacks based on our model. We theoretically and experimentally demonstrate the effectiveness of our attacks. For instance, we observe that, in a real-world large-scale dataset with 1.1 million users, our attack can correctly infer the cities a user lived in for 57% of the users, via confidence estimation, we are able to increase the attack success rate to over 90% if the attacker selectively attacks a half of the users. Moreover, we show that our attack can correctly infer attributes for significantly more users than previous attacks.

Connected through hyperedge HE-1 (induced by source paper "everything about you a multimodal approach towards friendship inference in online social networks"):
  with members:
    - "language in our time an empirical analysis of hashtags"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "you are your metadata identification and obfuscation of social media users using metadata information"
    - "graph embedding techniques applications and performance a survey"
    - "a hybrid model for identity obfuscation by face replacement"
    - "attriguard a practical defense against attribute inference attacks via adversarial machine learning"
    - "friendships rivalries and trysts characterizing relations between ideas in texts"
    - "connecting pixels to privacy and utility automatic redaction of private information in images"

Connected through hyperedge HE-2 (induced by source paper "privacy aware recommendation with private attribute protection using adversarial learning"):
  with members:
    - "less is more semi supervised causal inference for detecting pathogenic users in social media"
    - "i am not what i write privacy preserving text representation learning"
    - "hawkes process for understanding the influence of pathogenic social media accounts"
    - "detection of violent extremists in social media"
    - "privacy in social media identification mitigation and applications"
    - "similar but different exploiting users congruity for recommendation systems"
    - "protecting user privacy an approach for untraceable web browsing history and unambiguous user profiles"
    - "early identification of pathogenic social media accounts"

Connected through hyperedge HE-3 (induced by source paper "defending against machine learning based inference attacks via adversarial examples opportunities and challenges"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "misleading authorship attribution of source code using adversarial learning"
    - "mockingbird defending against deep learning based website fingerprinting attacks with adversarial traces"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "generative adversarial privacy"
    - "poisoning attacks to graph based recommender systems"
    - "graph based security and privacy analytics via collective classification with joint weight learning and propagation"
    - "adversarial binaries for authorship identification"

Connected through hyperedge HE-4 (induced by source paper "memguard defending against black box membership inference attacks via adversarial examples"):
  with members:
    - "defending against machine learning based inference attacks via adversarial examples opportunities and challenges"
    - "differentially private model publishing for deep learning"
    - "misleading authorship attribution of source code using adversarial learning"
    - "evaluating differentially private machine learning in practice"
    - "under the hood of membership inference attacks on aggregate location time series"
    - "updates leak data set inference and reconstruction attacks in online learning"
    - "exploiting unintended feature leakage in collaborative learning"
    - "tagvisor a privacy advisor for sharing hashtags"

Connected through hyperedge HE-5 (induced by source paper "attacking graph based classification via manipulating the graph structure"):
  with members:
    - "adversarial attacks on graph neural networks via meta learning"
    - "certifiable robustness and robust training for graph convolutional networks"
    - "adversarial attack on graph structured data"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "poisoning attacks to graph based recommender systems"
    - "graph based security and privacy analytics via collective classification with joint weight learning and propagation"
    - "structure based sybil detection in social networks via local rule based propagation"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"

Connected through hyperedge HE-6 (induced by source paper "adversarial privacy preservation under attribute inference attack"):
  with members:
    - "on learning invariant representation for domain adaptation"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "privacy preserving distributed deep learning for clinical data"
    - "age progression regression by conditional adversarial autoencoder"
    - "minimax filter learning to preserve privacy from inference attacks"
    - "equality of opportunity in supervised learning"
    - "wide residual networks"

Connected through hyperedge HE-7 (induced by source paper "data poisoning attacks on neighborhood based recommender systems"):
  with members:
    - "data poisoning against differentially private learners attacks and defenses"
    - "data poisoning attack against knowledge graph embedding"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "poisoning attacks to graph based recommender systems"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "data poisoning attacks on factorization based collaborative filtering"
    - "adversarial machine learning at scale"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-8 (induced by source paper "privacy in social media identification mitigation and applications"):
  with members:
    - "social media and user privacy"
    - "similar but different exploiting users congruity for recommendation systems"
    - "a survey of location prediction on twitter"
    - "securing social media user data an adversarial approach"
    - "blind de anonymization attacks using social networks"
    - "walk2friends inferring social links from mobility profiles"
    - "on content based recommendation and user privacy in social tagging systems"
    - "identifying community structures in dynamic 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)