human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "deducing truth from correlation"
  Abstract: This work is motivated by a question at the heart of unsupervised learning approaches: Assume we are collecting a number K of (subjective) opinions about some event E from K different agents. Can we infer E from them? Prima facie this seems impossible, since the agents may be lying. We model this task by letting the events be distributed according to some distribution p and the task is to estimate p under unknown noise. Again, this is impossible without additional assumptions. We report here the finding of very natural such assumptions - the availability of multiple copies of the true data, each under independent and invertible (in the sense of matrices) noise, is already sufficient: If the true distribution and the observations are modeled on the same finite alphabet, then the number of such copies needed to determine p to the highest possible precision is exactly three! This result can be seen as a counterpart to independent component analysis. Therefore, we call our approach 'dependent component analysis'. In addition, we present generalizations of the model to different alphabet sizes at in- and output. A second result is found: the 'activation' of invertibility through multiple parallel uses.

Connected through hyperedge HE-1 (induced by source paper "ultra reliable communication over arbitrarily varying channels under block restricted jamming"):
  with members:
    - "the arbitrarily varying wiretap channel secret randomness stability and super activation"
    - "telling truth from correlation"

Connected through hyperedge HE-2 (induced by source paper "information theoretic principles of universal discrete denoising"):
  with members:
    - "the rate distortion risk in estimation from compressed data"
    - "universal compressed sensing"
    - "tensor decompositions for learning latent variable models"
    - "algorithms for discrete denoising under channel uncertainty"
    - "universal minimax discrete denoising under channel uncertainty"

Connected through hyperedge HE-3 (induced by source paper "reliable communication under the influence of a state constrained jammer a novel perspective on receive diversity"):
  with members:
    - "the arbitrarily varying wiretap channel secret randomness stability and super activation"
    - "the arbitrarily varying wiretap channel deterministic and correlated random coding capacities under the strong secrecy criterion"
    - "the arbitrarily varying multiple access channel with conferencing encoders"

Question: Please tell me which class the center node 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)