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: "deterministic compression with uncertain priors"
  Abstract: We consider the task of compression of information when the source of the information and the destination do not agree on the prior, i.e., the distribution from which the information is being generated. This setting was considered previously by Kalai et al. (ICS 2011) who suggested that this was a natural model for human communication, and efficient schemes for compression here could give insights into the behavior of natural languages. Kalai et al. gave a compression scheme with nearly optimal performance, assuming the source and destination share some uniform randomness. In this work we explore the need for this randomness, and give some non-trivial upper bounds on the deterministic communication complexity for this problem. In the process we introduce a new family of structured graphs of constant fractional chromatic number whose (integral) chromatic number turns out to be a key component in the analysis of the communication complexity. We provide some non-trivial upper bounds on the chromatic number of these graphs to get our upper bound, while using lower bounds on variants of these graphs to prove lower bounds for some natural approaches to solve the communication complexity question. Tight analysis of communication complexity of our problems and the chromatic number of the underlying graphs remains open.

Connected through hyperedge HE-1 (induced by source paper "the price of uncertain priors in source coding"):
  with members:
    - "communication with imperfectly shared randomness"
    - "information equals amortized communication"

Connected through hyperedge HE-2 (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"
    - "communication with imperfectly shared randomness"
    - "information equals amortized communication"
    - "on extracting common random bits from correlated sources"

Connected through hyperedge HE-3 (induced by source paper "communication with imperfectly shared randomness"):
  with members:
    - "on the role of shared randomness in simultaneous communication"
    - "on the duality of additivity and tensorization"
    - "minimum hgr correlation principle from marginals to joint distribution"
    - "analysis of boolean functions"
    - "on extracting common random bits from correlated sources on large alphabets"
    - "on extracting common random bits from correlated sources"

Connected through hyperedge HE-4 (induced by source paper "information equals amortized communication"):
  with members:
    - "a strong direct product theorem for disjointness"
    - "communication requirements for generating correlated random variables"
    - "a direct sum theorem in communication complexity via message compression"

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)