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: "unequal error protection querying policies for the noisy 20 questions problem"
  Abstract: In this paper, we propose an open-loop unequal-error-protection querying policy based on superposition coding for the noisy 20 questions problem. In this problem, a player wishes to successively refine an estimate of the value of a continuous random variable by posing binary queries and receiving noisy responses. When the queries are designed non-adaptively as a single block and the noisy responses are modeled as the output of a binary symmetric channel, the 20 questions problem can be mapped to an equivalent problem of channel coding with unequal error protection (UEP). A new non-adaptive querying strategy based on UEP superposition coding is introduced, whose estimation error decreases with an exponential rate of convergence that is significantly better than that of the UEP repetition coding introduced by Variani  et al.  (2015). With the proposed querying strategy, the rate of exponential decrease in the number of queries matches the rate of a closed-loop adaptive scheme, where queries are sequentially designed with the benefit of feedback. Furthermore, the achievable error exponent is significantly better than that of random block codes employing equal error protection.

Connected through hyperedge HE-1 (induced by source paper "resolution limits of noisy 20 questions estimation"):
  with members:
    - "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"
    - "collaborative 20 questions for target localization"

Connected through hyperedge HE-2 (induced by source paper "parity queries for binary classification"):
  with members:
    - "community recovery in hypergraphs"
    - "predicting positive and negative links with noisy queries theory practice"
    - "fundamental limits on data acquisition trade offs between sample complexity and query difficulty"
    - "the set of solutions of random xorsat formulae"
    - "information recovery from pairwise measurements"
    - "decoding binary node labels from censored edge measurements phase transition and efficient recovery"
    - "boolean compressed sensing and noisy group testing"
    - "group testing with probabilistic tests theory design and application"

Connected through hyperedge HE-3 (induced by source paper "fundamental limits on data acquisition trade offs between sample complexity and query difficulty"):
  with members:
    - "community recovery in hypergraphs"
    - "information recovery from pairwise measurements"
    - "collaborative 20 questions for target localization"
    - "budget optimal task allocation for reliable crowdsourcing systems"

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)