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: "communication with imperfectly shared randomness"
  Abstract: The communication complexity of many fundamental problems reduces greatly when the communicating parties share randomness that is independent of the inputs to the communication task. Natural communication processes (say between humans) however often involve large amounts of shared correlations among the communicating players, but rarely allow for perfect sharing of randomness. Can the communication complexity benefit from shared correlations as well as it does from shared randomness? This question was considered mainly in the context of simultaneous communication by Bavarian et al. (ICALP 2014). In this work we study this problem in the standard interactive setting and give some general results. In particular, we show that every problem with communication complexity of $k$ bits with perfectly shared randomness has a protocol using imperfectly shared randomness with complexity $\exp(k)$ bits. We also show that this is best possible by exhibiting a promise problem with complexity $k$ bits with perfectly shared randomness which requires $\exp(k)$ bits when the randomness is imperfectly shared. Along the way we also highlight some other basic problems such as compression, and agreement distillation, where shared randomness plays a central role and analyze the complexity of these problems in the imperfectly shared randomness model. #R##N#The technical highlight of this work is the lower bound that goes into the result showing the tightness of our general connection. This result builds on the intuition that communication with imperfectly shared randomness needs to be less sensitive to its random inputs than communication with perfectly shared randomness. The formal proof invokes results about the small-set expansion of the noisy hypercube and an invariance principle to convert this intuition to a proof, thus giving a new application domain for these fundamental results.

Connected through hyperedge HE-1 (induced by source paper "round complexity of common randomness generation the amortized setting"):
  with members:
    - "communication rounds tradeoffs for common randomness and secret key generation"
    - "resource efficient common randomness and secret key schemes"
    - "the power of shared randomness in uncertain communication"
    - "secret key generation with limited interaction"
    - "on the role of shared randomness in simultaneous communication"
    - "secret key generation with one communicator and a one shot converse via hypercontractivity"
    - "communication complexity of permutation invariant functions"
    - "common information and secret key capacity"

Connected through hyperedge HE-2 (induced by source paper "universal communication universal graphs and graph labeling"):
  with members:
    - "near log convexity of measured heat in discrete time and consequences"
    - "the power of shared randomness in uncertain communication"
    - "an optimal ancestry labeling scheme with applications to xml trees and universal posets"
    - "on the implicit graph conjecture"
    - "communication with contextual uncertainty"
    - "optimal induced universal graphs and adjacency labeling for trees"
    - "contact representations of graphs in 3d"
    - "sublinear distance labeling"

Connected through hyperedge HE-3 (induced by source paper "communication complexity of estimating correlations"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "strong data processing inequalities for channels and bayesian networks"
    - "secret key generation with limited interaction"
    - "smoothing brascamp lieb inequalities and strong converses for common randomness generation"
    - "secret key generation with one communicator and a one shot converse via hypercontractivity"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "converses for secret key agreement and secure computing"
    - "an optimal lower bound on the communication complexity of gap hamming distance"

Connected through hyperedge HE-4 (induced by source paper "the price of uncertain priors in source coding"):
  with members:
    - "deterministic compression with uncertain priors"
    - "information equals amortized communication"

Connected through hyperedge HE-5 (induced by source paper "decidability of non interactive simulation of joint distributions"):
  with members:
    - "on the role of shared randomness in simultaneous communication"
    - "information complexity is computable"
    - "on the duality of additivity and tensorization"
    - "on non interactive simulation of joint distributions"
    - "communication complexity of permutation invariant functions"
    - "information equals amortized communication"
    - "reductions between expansion problems"
    - "a regularity lemma and low weight approximators for low degree polynomial threshold functions"

Connected through hyperedge HE-6 (induced by source paper "communication complexity of permutation invariant functions"):
  with members:
    - "on the role of shared randomness in simultaneous communication"
    - "information complexity is computable"
    - "on the communication complexity of sparse set disjointness and exists equal problems"
    - "lower bounds on information complexity via zero communication protocols and applications"
    - "on extracting common random bits from correlated sources on large alphabets"
    - "an optimal lower bound on the communication complexity of gap hamming distance"
    - "on extracting common random bits from correlated sources"
    - "the partition bound for classical communication complexity and query complexity"

Connected through hyperedge HE-7 (induced by source paper "communication with contextual uncertainty"):
  with members:
    - "the power of shared randomness in uncertain communication"
    - "on the role of shared randomness in simultaneous communication"
    - "communication complexity of permutation invariant functions"
    - "deterministic compression with uncertain priors"
    - "information equals amortized communication"
    - "on extracting common random bits from correlated sources"

Connected through hyperedge HE-8 (induced by source paper "on the role of shared randomness in simultaneous communication"):
  with members:
    - "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"

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.CC(Computational Complexity)