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 "private set intersection a multi message symmetric private information retrieval perspective"):
  Abstract: We study the problem of private set intersection (PSI). In this problem, there are two entities $E_i$, for $i=1, 2$, each storing a set $\mathcal{P}_i$, whose elements are picked from a finite field $\mathbb{F}_K$, on $N_i$ replicated and non-colluding databases. It is required to determine the set intersection $\mathcal{P}_1 \cap \mathcal{P}_2$ without leaking any information about the remaining elements to the other entity with the least amount of downloaded bits. We first show that the PSI problem can be recast as a multi-message symmetric private information retrieval (MM-SPIR) problem. Next, as a stand-alone result, we derive the information-theoretic sum capacity of MM-SPIR, $C_{MM-SPIR}$. We show that with $K$ messages, $N$ databases, and the size of the desired message set $P$, the exact capacity of MM-SPIR is $C_{MM-SPIR} = 1 - \frac{1}{N}$ when $P \leq K-1$, provided that the entropy of the common randomness $S$ satisfies $H(S) \geq \frac{P}{N-1}$ per desired symbol. This result implies that there is no gain for MM-SPIR over successive single-message SPIR (SM-SPIR). For the MM-SPIR problem, we present a novel capacity-achieving scheme that builds on the near-optimal scheme of Banawan-Ulukus originally proposed for the multi-message PIR (MM-PIR) problem without database privacy constraints. Surprisingly, our scheme here is exactly optimal for the MM-SPIR problem for any $P$, in contrast to the scheme for the MM-PIR problem, which was proved only to be near-optimal. Our scheme is an alternative to the SM-SPIR scheme of Sun-Jafar. Based on this capacity result for MM-SPIR, and after addressing the added requirements in its conversion to the PSI problem, we show that the optimal download cost for the PSI problem is $\min\left\{\left\lceil\frac{P_1 N_2}{N_2-1}\right\rceil, \left\lceil\frac{P_2 N_1}{N_1-1}\right\rceil\right\}$, where $P_i$ is the cardinality of set $\mathcal{P}_i$

Includes members:
  Member "the capacity of private information retrieval from heterogeneous uncoded caching databases":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "latent variable private information retrieval")
  Member "on the storage cost of private information retrieval":
    also connected through other hyperedges:
      (none)
  Member "on the information leakage in private information retrieval systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the capacity of single server weakly private information retrieval")
  Member "capacity achieving private information retrieval codes from mds coded databases with minimum message size":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the information leakage in private information retrieval systems")
  Member "improved storage for efficient private information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the storage cost of private information retrieval")
  Member "staircase pir universally robust private information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the capacity of private information retrieval from heterogeneous uncoded caching databases")
  Member "noisy private information retrieval on separability of channel coding and information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "private information retrieval over gaussian mac")
  Member "the capacity of private information retrieval from uncoded storage constrained databases":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "latent variable private information retrieval")

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)