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 "distributed hypothesis testing with privacy constraints"):
  Abstract: We revisit the distributed hypothesis testing (or hypothesis testing with communication constraints) problem from the viewpoint of privacy. Instead of observing the raw data directly, the transmitter observes a sanitized or randomized version of it. We impose an upper bound on the mutual information between the raw and randomized data. Under this scenario, the receiver, which is also provided with side information, is required to make a decision on whether the null or alternative hypothesis is in effect. We first provide a general lower bound on the type-II exponent for an arbitrary pair of hypotheses. Next, we show that if the distribution under the alternative hypothesis is the product of the marginals of the distribution under the null (i.e., testing against independence), then the exponent is known exactly. Moreover, we show that the strong converse property holds. Using ideas from Euclidean information theory, we also provide an approximate expression for the exponent when the communication rate is low and the privacy level is high. Finally, we illustrate our results with a binary and a Gaussian example.

Includes members:
  Member "technical privacy metrics a systematic survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ensuring privacy in location based services a model based approach")
  Member "on hypothesis testing against independence with multiple decision centers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed hypothesis testing cooperation and concurrent detection")
  Member "hypothesis testing under mutual information privacy constraints in the high privacy regime":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "privacy aware distributed hypothesis testing")
  Member "hypothesis testing under maximal leakage privacy constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a practical approach to navigating the tradeoff between privacy and precise utility")
  Member "distributed hypothesis testing over noisy channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "privacy aware distributed hypothesis testing")
  Member "neyman pearson test for zero rate multiterminal hypothesis testing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed hypothesis testing cooperation and concurrent detection")
  Member "euclidean information theory of networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "principal inertia components and applications")
  Member "utility privacy tradeoffs in databases an information theoretic approach":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a practical approach to navigating the tradeoff between privacy and precise utility")

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)