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: "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.

Connected through hyperedge HE-1 (induced by source paper "theoretical limitations of self attention in neural sequence models"):
  with members:
    - "assessing the ability of self attention networks to learn word order"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "bert rediscovers the classical nlp pipeline"
    - "on the computational power of rnns"
    - "berts of a feather do not generalize together large variability in generalization across models with similar test set performance"
    - "open sesame getting inside bert s linguistic knowledge"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"

Connected through hyperedge HE-2 (induced by source paper "sensitivity conjecture and log rank conjecture for functions with small alternating numbers"):
  with members:
    - "sensitivity versus certificate complexity of boolean functions"
    - "size of sets with small sensitivity a generalization of simon s lemma"
    - "parity decision tree complexity and 4 party communication complexity of xor functions are polynomially equivalent"
    - "tighter relations between sensitivity and other complexity measures"
    - "learning circuits with few negations"
    - "a composition theorem for parity kill number"
    - "communication is bounded by root of rank"
    - "fourier sparsity spectral norm and the log rank conjecture"

Connected through hyperedge HE-3 (induced by source paper "low sensitivity functions from unambiguous certificates"):
  with members:
    - "randomized query complexity of sabotaged and composed functions"
    - "nearly optimal separations between communication or query complexity and partitions"
    - "degree and sensitivity tails of two distributions"
    - "separations in query complexity based on pointer functions"
    - "sensitivity versus certificate complexity of boolean functions"
    - "size of sets with small sensitivity a generalization of simon s lemma"
    - "an o n 0 4732 upper bound on the complexity of the gks communication game"
    - "separating decision tree complexity from subcube partition complexity"

Connected through hyperedge HE-4 (induced by source paper "on the sensitivity conjecture for disjunctive normal forms"):
  with members:
    - "tighter relations between sensitivity and other complexity measures"
    - "composition limits and separating examples for some boolean function complexity measures"
    - "new separation between s f and bs f"
    - "variations on the sensitivity conjecture"
    - "on the sensitivity of cyclically invariant boolean functions"

Connected through hyperedge HE-5 (induced by source paper "degree and sensitivity tails of two distributions"):
  with members:
    - "size of sets with small sensitivity a generalization of simon s lemma"
    - "tighter relations between sensitivity and other complexity measures"
    - "representation learning a review and new perspectives"

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)