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 "privacy aware distributed hypothesis testing"):
  Abstract: A distributed binary hypothesis testing (HT) problem involving two parties, a remote observer and a detector, is studied. The remote observer has access to a discrete memoryless source, and communicates its observations to the detector via a rate-limited noiseless channel. The detector observes another discrete memoryless source, and performs a binary hypothesis test on the joint distribution of its own observations with those of the observer. While the goal of the observer is to maximize the type II error exponent of the test for a given type I error probability constraint, it also wants to keep a private part of its observations as oblivious to the detector as possible. Considering both equivocation and average distortion as possible measures of privacy, the trade-off between the communication rate from the observer to the detector, the type II error exponent, and privacy is studied. For the general HT problem, we establish single-letter inner bounds on both the rate-error exponent-equivocation and rate-error exponent-distortion trade-offs. Subsequently, single-letter characterizations for both trade-offs are obtained (i) for testing against conditional independence of the observer's observations from those of the detector, given some additional side-information at the detector; and (ii) when the communication rate constraint over the channel is zero. Finally, we show by providing a counterexample that, the strong converse which holds for distributed HT without a privacy constraint, does not hold when a privacy constraint is imposed. This implies that, in general, the rate-error exponent-equivocation and rate-error exponent-distortion trade-offs are not independent of the type I error probability constraint.

Includes members:
  Member "privacy aware smart metering progress and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "privacy aware time series data sharing with deep reinforcement learning")
  Member "distributed hypothesis testing with privacy constraints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed hypothesis testing cooperation and concurrent detection")
  Member "technical privacy metrics a systematic survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ensuring privacy in location based services a model based approach")
  Member "distributed hypothesis testing based on unequal error protection codes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed hypothesis testing with a privacy constraint")
  Member "privacy against a hypothesis testing adversary":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed hypothesis testing with a privacy constraint")
  Member "on the reliability function of distributed hypothesis testing under optimal detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed hypothesis testing with variable length coding")
  Member "distributed hypothesis testing over discrete memoryless channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hypothesis testing over the two hop relay network")
  Member "hypothesis testing under mutual information privacy constraints in the high privacy regime":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "shredder learning noise to protect privacy with partial dnn inference on the edge")

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)