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 "information theoretically secure databases"):
  Abstract: We introduce the notion of a database system that is information theoretically secure in between accesses—a database system with the properties that 1) users can efficiently access their data, and 2) while a user is not accessing their data, the user’s information is information theoretically secure to malicious agents, provided that certain requirements on the maintenance of the database are realized. We stress that the security guarantee is information theoretic and everlasting: it relies neither on unproved hardness assumptions, nor on the assumption that the adversary is computationally or storage bounded. We propose a realization of such a database system and prove that a user’s stored information, in between times when it is being legitimately accessed, is information theoretically secure both to adversaries who interact with the database in the prescribed manner, as well as to adversaries who have installed a virus that has access to the entire database and communicates with the adversary. The central idea behind our design of an information theoretically secure database system is the construction of a “re-randomizing database” that periodically changes the internal representation of the information that is being stored. To ensure security, these remappings of the representation of the data must be made sufficiently often in comparison to the amount of information that is being communicated from the database between remappings and the amount of local memory in the database that a virus may preserve during the remappings. While this changing representation provably foils the ability of an adversary to glean information, it can be accomplished in a manner transparent to the legitimate users, preserving how database users access their data. The core of the proof of the security guarantee is the following communication/data tradeoff for the problem of learning sparse parities from uniformly random n-bit examples. Fix a set S ⊂ {1, . . . , n} of size k: given access to examples x1, . . . , xt where xi ∈ {0, 1} is chosen uniformly at random, conditioned on the XOR of the components of x indexed by set S equalling 0, any algorithm that learns the set S with probability at least p and extracts at most r bits of information from each example, must see at least p · ( n r )k/2 ck examples, for ck ≥ 14 · √ (2e)k kk+3 . The r bits of information extracted from each example can be an arbitrary (adaptively chosen) function of the entire example, and need not be simply a subset of the bits of the example.

Includes members:
  Member "fast learning requires good memory a time space lower bound for parity learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "time space tradeoffs for distinguishing distributions and applications to security of goldreich s prg")
  Member "communication lower bounds for statistical estimation problems via a distributed data processing inequality":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "locally private hypothesis selection")
  Member "on communication cost of distributed statistical estimation and dimensionality":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the communication complexity of optimization")
  Member "fundamental limits of online and distributed algorithms for statistical learning and estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "time space tradeoffs for distinguishing distributions and applications to security of goldreich s prg")
  Member "distributed learning communication complexity and privacy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on distributed machine learning")

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.CR(Cryptography and Security)