human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "communication with contextual uncertainty"):
  Abstract: We introduce a simple model illustrating the role of context in communication and the challenge posed by uncertainty of knowledge of context. We consider a variant of distributional communication complexity where Alice gets some information $x$ and Bob gets $y$, where $(x,y)$ is drawn from a known distribution, and Bob wishes to compute some function $g(x,y)$ (with high probability over $(x,y)$). In our variant, Alice does not know $g$, but only knows some function $f$ which is an approximation of $g$. Thus, the function being computed forms the context for the communication, and knowing it imperfectly models (mild) uncertainty in this context. #R##N#A naive solution would be for Alice and Bob to first agree on some common function $h$ that is close to both $f$ and $g$ and then use a protocol for $h$ to compute $h(x,y)$. We show that any such agreement leads to a large overhead in communication ruling out such a universal solution. #R##N#In contrast, we show that if $g$ has a one-way communication protocol with complexity $k$ in the standard setting, then it has a communication protocol with complexity $O(k \cdot (1+I))$ in the uncertain setting, where $I$ denotes the mutual information between $x$ and $y$. In the particular case where the input distribution is a product distribution, the protocol in the uncertain setting only incurs a constant factor blow-up in communication and error. #R##N#Furthermore, we show that the dependence on the mutual information $I$ is required. Namely, we construct a class of functions along with a non-product distribution over $(x,y)$ for which the communication complexity is a single bit in the standard setting but at least $\Omega(\sqrt{n})$ bits in the uncertain setting.

Includes members:
  Member "the power of shared randomness in uncertain communication":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "round complexity of common randomness generation the amortized setting")
  Member "on the role of shared randomness in simultaneous communication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "round complexity of common randomness generation the amortized setting")
  Member "communication complexity of permutation invariant functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "round complexity of common randomness generation the amortized setting")
  Member "communication with imperfectly shared randomness":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "round complexity of common randomness generation the amortized setting")
  Member "deterministic compression with uncertain priors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the price of uncertain priors in source coding")
  Member "information equals amortized communication":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "communication for generating correlation a unifying survey")
  Member "on extracting common random bits from correlated sources":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "communication for generating correlation a unifying survey")

Question: Please tell me which class the source paper 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)