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: "active sequential hypothesis testing"
  Abstract: Consider a decision maker who is responsible to dynamically collect observations so as to enhance his information about an underlying phenomena of interest in a speedy manner while accounting for the penalty of wrong declaration. Due to the sequential nature of the problem, the decision maker relies on his current information state to adaptively select the most ``informative'' sensing action among the available ones. In this paper, using results in dynamic programming, lower bounds for the optimal total cost are established. The lower bounds characterize the fundamental limits on the maximum achievable information acquisition rate and the optimal reliability. Moreover, upper bounds are obtained via an analysis of two heuristic policies for dynamic selection of actions. It is shown that the first proposed heuristic achieves asymptotic optimality, where the notion of asymptotic optimality, due to Chernoff, implies that the relative difference between the total cost achieved by the proposed policy and the optimal total cost approaches zero as the penalty of wrong declaration (hence the number of collected samples) increases. The second heuristic is shown to achieve asymptotic optimality only in a limited setting such as the problem of a noisy dynamic search. However, by considering the dependency on the number of hypotheses, under a technical condition, this second heuristic is shown to achieve a nonzero information acquisition rate, establishing a lower bound for the maximum achievable rate and error exponent. In the case of a noisy dynamic search with size-independent noise, the obtained nonzero rate and error exponent are shown to be maximum.

Connected through hyperedge HE-1 (induced by source paper "decentralized sequential active hypothesis testing and the mac feedback capacity"):
  with members:
    - "extrinsic jensen shannon divergence applications to variable length coding"
    - "decentralized stochastic control with partial history sharing a common information approach"

Connected through hyperedge HE-2 (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-3 (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"
    - "collaborative 20 questions for target localization"
    - "bit wise unequal error protection for variable length block codes with feedback"
    - "information theoretic viewpoints on optimal causal coding decoding problems"

Connected through hyperedge HE-4 (induced by source paper "searching for anomalies over composite hypotheses"):
  with members:
    - "active anomaly detection in heterogeneous processes"
    - "learning to detect an oddball target"
    - "optimal index policies for anomaly localization in resource constrained cyber systems"
    - "asymptotically optimal anomaly detection via sequential testing"
    - "sequentiality and adaptivity gains in active hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "sequential testing for sparse recovery"

Connected through hyperedge HE-5 (induced by source paper "active hypothesis testing beyond chernoff stein"):
  with members:
    - "policy design for active sequential hypothesis testing using deep learning"
    - "controlled sensing for multihypothesis testing"

Connected through hyperedge HE-6 (induced by source paper "learning to detect an odd markov arm"):
  with members:
    - "learning to detect an oddball target with observations from an exponential family"
    - "learning to detect an oddball target"

Connected through hyperedge HE-7 (induced by source paper "detection under one bit messaging over adaptive networks"):
  with members:
    - "quantized consensus by the admm probabilistic versus deterministic quantizers"
    - "distributed detection over adaptive networks refined asymptotics and the role of connectivity"
    - "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime"
    - "distributed parameter estimation with quantized communication via running average"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "distributed detection over noisy networks large deviations analysis"
    - "diffusion adaptation over networks"

Connected through hyperedge HE-8 (induced by source paper "policy design for active sequential hypothesis testing using deep learning"):
  with members:
    - "deep learning for joint source channel coding of text"
    - "detection algorithms for communication systems using deep learning"
    - "qmdp net deep learning for planning under partial observability"
    - "deep recurrent q learning for partially observable mdps"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "controlled sensing for multihypothesis testing"

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)