human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "everything about you a multimodal approach towards friendship inference in online social networks"):
  Abstract: Most previous works in privacy of Online Social Networks (OSN) focus on a restricted scenario of using one type of information to infer another type of information or using only static profile data such as username, profile picture or home location. However the multimedia footprints of users has become extremely diverse nowadays. In reality, an adversary would exploit all types of information obtainable over time, to achieve its goal. In this paper, we analyse OSN privacy by jointly exploiting longterm multimodal information. We focus in particular on inference of social relationships. We consider five popular components of posts shared by users, namely images, hashtags, captions, geo-locations and published friendships. Large scale evaluation on a real-world OSN dataset shows that while our monomodal attacks achieve strong predictions, our multimodal attack leads to a stronger performance with AUC (area under the ROC curve) above 0.9. Our results highlight the need for multimodal obfuscation approaches towards protecting privacy in an era where multimedia footprints of users get increasingly diverse.

Includes members:
  Member "language in our time an empirical analysis of hashtags":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "how much and when do we need higher order information in hypergraphs a case study on hyperedge prediction")
  Member "tagvisor a privacy advisor for sharing hashtags":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "you are your metadata identification and obfuscation of social media users using metadata information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "language in our time an empirical analysis of hashtags")
  Member "graph embedding techniques applications and performance a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "a hybrid model for identity obfuscation by face replacement":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fawkes protecting personal privacy against unauthorized deep learning models")
  Member "attriguard a practical defense against attribute inference attacks via adversarial machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "friendships rivalries and trysts characterizing relations between ideas in texts":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "no permanent friends or enemies tracking relationships between nations from news")
  Member "connecting pixels to privacy and utility automatic redaction of private information in images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "shape evasion preventing body shape inference of multi stage approaches")

Question: Please tell me which class the source paper 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)