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 "smooth boolean functions are easy efficient algorithms for low sensitivity functions"):
  Abstract: A natural measure of smoothness of a Boolean function is its sensitivity (the largest number of Hamming neighbors of a point which differ from it in function value). The structure of smooth or equivalently low-sensitivity functions is still a mystery. A well-known conjecture states that every such Boolean function can be computed by a shallow decision tree. While this conjecture implies that smooth functions are easy to compute in the simplest computational model, to date no non-trivial upper bounds were known for such functions in any computational model, including unrestricted Boolean circuits. Even a bound on the description length of such functions better than the trivial $2^n$ does not seem to have been known. #R##N#In this work, we establish the first computational upper bounds on smooth Boolean functions: #R##N#1) We show that every sensitivity s function is uniquely specified by its values on a Hamming ball of radius 2s. We use this to show that such functions can be computed by circuits of size $n^{O(s)}$. #R##N#2) We show that sensitivity s functions satisfy a strong pointwise noise-stability guarantee for random noise of rate O(1/s). We use this to show that these functions have formulas of depth O(s log n). #R##N#3) We show that sensitivity s functions can be (locally) self-corrected from worst-case noise of rate $\exp(-O(s))$. #R##N#All our results are simple, and follow rather directly from (variants of) the basic fact that that the function value at few points in small neighborhoods of a given point determine its function value via a majority vote. Our results confirm various consequences of the conjecture. They may be viewed as providing a new form of evidence towards its validity, as well as new directions towards attacking it.

Includes members:
  Member "sensitivity versus certificate complexity of boolean functions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "alternation sparsity and sensitivity bounds and exponential gaps")
  Member "size of sets with small sensitivity a generalization of simon s lemma":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sensitivity conjecture and log rank conjecture for functions with small alternating numbers")
  Member "a tight lower bound on certificate complexity in terms of block sensitivity and sensitivity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "low sensitivity functions from unambiguous certificates")
  Member "analysis of boolean functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "coarse grained complexity for dynamic algorithms")
  Member "tighter relations between sensitivity and other complexity measures":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sensitivity conjecture and log rank conjecture for functions with small alternating numbers")
  Member "new separation between s f and bs f":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sensitivity conjecture and log rank conjecture for functions with small alternating numbers")
  Member "variations on the sensitivity conjecture":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "alternation sparsity and sensitivity bounds and exponential gaps")

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)