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: "collaborative 20 questions for target localization"
  Abstract: We consider the problem of 20 questions with noise for multiple players under the minimum entropy criterion in the setting of stochastic search, with application to target localization. Each player yields a noisy response to a binary query governed by a certain error probability. First, we propose a sequential policy for constructing questions that queries each player in sequence and refines the posterior of the target location. Second, we consider a joint policy that asks all players questions in parallel at each time instant and characterize the structure of the optimal policy for constructing the sequence of questions. This generalizes the single player probabilistic bisection method for stochastic search problems. Third, we prove an equivalence between the two schemes showing that, despite the fact that the sequential scheme has access to a more refined filtration, the joint scheme performs just as well on average. Fourth, we establish convergence rates of the mean-square error and derive error exponents. Finally, we obtain an extension to the case of unknown error probabilities. This framework provides a mathematical model for incorporating a human in the loop for active machine learning systems.

Connected through hyperedge HE-1 (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"
    - "searching with measurement dependent noise"
    - "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-2 (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"
    - "searching with measurement dependent noise"

Connected through hyperedge HE-3 (induced by source paper "active query driven visual search using probabilistic bisection and convolutional neural networks"):
  with members:
    - "how convolutional neural network see the world a survey of convolutional neural network visualization methods"
    - "distributed probabilistic bisection search using social learning"
    - "you only look once unified real time object detection"
    - "deep learning face attributes in the wild"
    - "on decentralized estimation with active queries"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "active testing for face detection and localization"

Connected through hyperedge HE-4 (induced by source paper "unequal error protection querying policies for the noisy 20 questions problem"):
  with members:
    - "non adaptive policies for 20 questions target localization"
    - "error exponents for broadcast channels with degraded message sets"

Connected through hyperedge HE-5 (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"
    - "searching with measurement dependent noise"
    - "near optimal adaptive compressed sensing"
    - "optimal multiband joint detection for spectrum sensing in cognitive radio networks"

Connected through hyperedge HE-6 (induced by source paper "fundamental limits on data acquisition trade offs between sample complexity and query difficulty"):
  with members:
    - "unequal error protection querying policies for the noisy 20 questions problem"
    - "community recovery in hypergraphs"
    - "information recovery from pairwise measurements"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-7 (induced by source paper "bounds on variance for unimodal distributions"):
  with members:
    - "forward and reverse entropy power inequalities in convex geometry"
    - "bounds on variance for symmetric unimodal distributions"
    - "the entropy per coordinate of a random vector is highly constrained under convexity conditions"

Connected through hyperedge HE-8 (induced by source paper "distributed probabilistic bisection search using social learning"):
  with members:
    - "probabilistic group testing under sum observations a parallelizable 2 approximation for entropy loss"
    - "on decentralized estimation with active queries"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "active testing for face detection and localization"
    - "gossip algorithms for distributed signal processing"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

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)