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: "on the role of shared randomness in simultaneous communication"
  Abstract: Two parties wish to carry out certain distributed computational tasks, and they are given access to a source of correlated random bits. It allows the parties to act in a correlated manner, which can be quite useful. But what happens if the shared randomness is not perfect? In this work, we initiate the study of the power of different sources of shared randomness in communication complexity. This is done in the setting of simultaneous message passing (SMP) model of communication complexity, which is one of the most suitable models for studying the resource of shared randomness. Toward characterising the power of various sources of shared randomness, we introduce a measure for the quality of a source - we call it collision complexity. Our results show that the collision complexity tightly characterises the power of a (shared) randomness resource in the SMP model. #R##N#Of independent interest is our demonstration that even the weakest sources of shared randomness can in some cases increase the power of SMP substantially: the equality function can be solved very efficiently with virtually any nontrivial shared randomness.

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"
    - "secret key generation with one communicator and a one shot converse via hypercontractivity"
    - "communication complexity of permutation invariant functions"
    - "communication with imperfectly shared randomness"
    - "common information and secret key capacity"

Connected through hyperedge HE-2 (induced by source paper "communication rounds tradeoffs for common randomness and secret key generation"):
  with members:
    - "dimension reduction for polynomials over gaussian space and applications"
    - "resource efficient common randomness and secret key schemes"
    - "decidability of non interactive simulation of joint distributions"
    - "on non interactive simulation of joint distributions"
    - "communication complexity of permutation invariant functions"
    - "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"
    - "common information and secret key capacity"
    - "on extracting common random bits from correlated sources on large alphabets"

Connected through hyperedge HE-3 (induced by source paper "resource efficient common randomness and secret key schemes"):
  with members:
    - "common randomness and key generation with limited interaction"
    - "secret key generation with one communicator and a one shot converse via hypercontractivity"
    - "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"
    - "on extracting common random bits from correlated sources on large alphabets"

Connected through hyperedge HE-4 (induced by source paper "the power of shared randomness in uncertain communication"):
  with members:
    - "communication with contextual uncertainty"
    - "communication complexity of permutation invariant functions"
    - "analysis of boolean functions"

Connected through hyperedge HE-5 (induced by source paper "decidability of non interactive simulation of joint distributions"):
  with members:
    - "information complexity is computable"
    - "on the duality of additivity and tensorization"
    - "on non interactive simulation of joint distributions"
    - "communication complexity of permutation invariant functions"
    - "communication with imperfectly shared randomness"
    - "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:
    - "information complexity is computable"
    - "communication with imperfectly shared randomness"
    - "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"
    - "communication complexity of permutation invariant functions"
    - "communication with imperfectly shared randomness"
    - "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 "communication with imperfectly shared randomness"):
  with members:
    - "on the duality of additivity and tensorization"
    - "minimum hgr correlation principle from marginals to joint distribution"
    - "analysis of boolean functions"
    - "deterministic compression with uncertain priors"
    - "on extracting common random bits from correlated sources on large alphabets"
    - "on extracting common random bits from correlated sources"

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)