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 "latent variable private information retrieval"):
  Abstract: In many applications, content accessed by users (movies, videos, news articles, etc.) can leak sensitive latent attributes, such as religious and political views, sexual orientation, ethnicity, gender, and others. To prevent such information leakage, the goal of classical PIR is to hide the identity of the content/message being accessed, which subsequently also hides the latent attributes. This solution, while private, can be too costly, particularly, when perfect (information-theoretic) privacy constraints are imposed. For instance, for a single database holding $K$ messages, privately retrieving one message is possible if and only if the user downloads the entire database of $K$ messages. Retrieving content privately, however, may not be necessary to perfectly hide the latent attributes. #R##N#Motivated by the above, we formulate and study the problem of latent-variable private information retrieval (LV-PIR), which aims at allowing the user efficiently retrieve one out of $K$ messages (indexed by $\theta$) without revealing any information about the latent variable (modeled by $S$). We focus on the practically relevant setting of a single database and show that one can significantly reduce the download cost of LV-PIR (compared to the classical PIR) based on the correlation between $\theta$ and $S$. We present a general scheme for LV-PIR as a function of the statistical relationship between $\theta$ and $S$, and also provide new results on the capacity/download cost of LV-PIR. Several open problems and new directions are also discussed.

Includes members:
  Member "x secure t private information retrieval from mds coded storage with byzantine and unresponsive servers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "the capacity of private information retrieval from heterogeneous uncoded caching databases":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "private set intersection a multi message symmetric private information retrieval perspective")
  Member "breaking the mds pir capacity barrier via joint storage coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards practical private information retrieval from mds array codes")
  Member "robust private information retrieval from coded systems with byzantine and colluding servers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "private sequential function computation")
  Member "the capacity of private information retrieval from uncoded storage constrained databases":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "private set intersection a multi message symmetric private information retrieval perspective")
  Member "an mds pir capacity achieving protocol for distributed storage using non mds linear codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "private sequential function computation")
  Member "private information retrieval from coded storage systems with colluding byzantine and unresponsive servers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards the capacity of private information retrieval from coded and colluding servers")
  Member "cross subspace alignment and the asymptotic capacity of x secure t private information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")

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.IT(Information Theory)