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: "simulating noisy channel interaction"
  Abstract: We show that $T$ rounds of interaction over the binary symmetric channel $BSC_{1/2-\epsilon}$ with feedback can be simulated with $O(\epsilon^2 T)$ rounds of interaction over a noiseless channel. We also introduce a more general "energy cost" model of interaction over a noisy channel. We show energy cost to be equivalent to external information complexity, which implies that our simulation results are unlikely to carry over to energy complexity. Our main technical innovation is a self-reduction from simulating a noisy channel to simulating a slightly-less-noisy channel, which may have other applications in the area of interactive compression.

Connected through hyperedge HE-1 (induced by source paper "exponential separations in local differential privacy"):
  with members:
    - "the role of interactivity in local differential privacy"
    - "learning without interaction requires separation"
    - "test without trust optimal locally private distribution testing"
    - "heavy hitters and the structure of local privacy"
    - "prochlo strong privacy for analytics in the crowd"
    - "collecting telemetry data privately"
    - "local private efficient protocols for succinct histograms"
    - "rappor randomized aggregatable privacy preserving ordinal response"

Connected through hyperedge HE-2 (induced by source paper "exponential separations in local differential privacy through communication complexity"):
  with members:
    - "learning without interaction requires separation"
    - "distributed differential privacy via shuffling"
    - "heavy hitters and the structure of local privacy"
    - "collecting telemetry data privately"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-3 (induced by source paper "competitive analysis of the top k ranking problem"):
  with members:
    - "top k ranking from pairwise comparisons when spectral ranking is optimal"
    - "adversarial top k ranking"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "multiple identifications in multi armed bandits"
    - "rank centrality ranking from pair wise comparisons"
    - "active ranking using pairwise comparisons"
    - "noisy sorting without resampling"

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)