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: "searching with measurement dependent noise"
  Abstract: Consider a target moving with a constant velocity on a unit-circumference circle, starting from an arbitrary location. To acquire the target, any region of the circle can be probed for its presence, but the associated measurement noise increases with the size of the probed region. We are interested in the expected time required to find the target to within some given resolution and error probability. For a known velocity, we characterize the optimal tradeoff between time and resolution (i.e., maximal rate), and show that in contrast to the case of constant measurement noise, measurement dependent noise incurs a multiplicative gap between adaptive search and non-adaptive search. Moreover, our adaptive scheme attains the optimal rate-reliability tradeoff. We further show that for optimal non-adaptive search, accounting for an unknown velocity incurs a factor of two in rate.

Connected through hyperedge HE-1 (induced by source paper "active learning and csi acquisition for mmwave initial alignment"):
  with members:
    - "improved target acquisition rates with feedback codes"
    - "linear block coding for efficient beam discovery in millimeter wave communication networks"
    - "overview of millimeter wave communications for fifth generation 5g wireless networks with a focus on propagation models"
    - "comparative analysis of initial access techniques in 5g mmwave cellular networks"
    - "hybrid beamforming for massive mimo a survey"
    - "active learning from imperfect labelers"
    - "an efficient feedback coding scheme with low error probability for discrete memoryless channels"
    - "a simple proof for the optimality of randomized posterior matching"

Connected through hyperedge HE-2 (induced by source paper "construction and analysis of posterior matching in arbitrary dimensions via optimal transport"):
  with members:
    - "variable length codes for channels with memory and feedback error exponent lower bounds"
    - "an efficient feedback coding scheme with low error probability for discrete memoryless channels"
    - "a simple proof for the optimality of randomized posterior matching"
    - "extrinsic jensen shannon divergence applications to variable length coding"
    - "collaborative 20 questions for target localization"
    - "bit wise unequal error protection for variable length block codes with feedback"
    - "active sequential hypothesis testing"
    - "information theoretic viewpoints on optimal causal coding decoding problems"

Connected through hyperedge HE-3 (induced by source paper "resolution limits of noisy 20 questions estimation"):
  with members:
    - "unequal error protection querying policies for the noisy 20 questions problem"
    - "improved target acquisition rates with feedback codes"
    - "exponential strong converse for content identification with lossy recovery"
    - "smoothing brascamp lieb inequalities and strong converses for common randomness generation"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "the dispersion of nearest neighbor decoding for additive non gaussian channels"
    - "second order coding rates for channels with state"
    - "collaborative 20 questions for target localization"

Connected through hyperedge HE-4 (induced by source paper "improved target acquisition rates with feedback codes"):
  with members:
    - "comparative analysis of initial access techniques in 5g mmwave cellular networks"
    - "directional initial access for millimeter wave cellular systems"
    - "extrinsic jensen shannon divergence applications to variable length coding"
    - "collaborative 20 questions for target localization"
    - "near optimal adaptive compressed sensing"
    - "optimal multiband joint detection for spectrum sensing in cognitive radio networks"

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)