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: "degree and sensitivity tails of two distributions"
  Abstract: The sensitivity of a Boolean function f is the maximum over all inputs x, of the number of sensitive coordinates of x. The well-known sensitivity conjecture of Nisan (see also Nisan and Szegedy) states that every sensitivity-s Boolean function can be computed by a polynomial over the reals of degree poly(s). The best known upper bounds on degree, however, are exponential rather than polynomial in s. #R##N#Our main result is an approximate version of the conjecture: every Boolean function with sensitivity s can be epsilon-approximated (in L_2) by a polynomial whose degree is O(s log(1/epsilon)). This is the first improvement on the folklore bound of s/epsilon. Further, we show that improving the bound to O(s^c log(1/epsilon)^d)$ for any d   0 will imply the sensitivity conjecture. Thus our result is essentially the best one can hope for without proving the conjecture. #R##N#We postulate a robust analogue of the sensitivity conjecture: if most inputs to a Boolean function f have low sensitivity, then most of the Fourier mass of f is concentrated on small subsets, and present an approach towards proving this conjecture.

Connected through hyperedge HE-1 (induced by source paper "alternation sparsity and sensitivity bounds and exponential gaps"):
  with members:
    - "on the sensitivity conjecture for disjunctive normal forms"
    - "sensitivity conjecture and log rank conjecture for functions with small alternating numbers"
    - "sensitivity versus certificate complexity of boolean functions"
    - "analysis of boolean functions"
    - "learning circuits with few negations"
    - "communication is bounded by root of rank"
    - "fourier sparsity spectral norm and the log rank conjecture"
    - "on the parity complexity measures of boolean functions"

Connected through hyperedge HE-2 (induced by source paper "improved bounds on fourier entropy and min entropy"):
  with members:
    - "on the fourier entropy influence conjecture for extremal classes"
    - "improved lower bounds for the fourier entropy influence conjecture via lexicographic functions"
    - "nearly optimal separations between communication or query complexity and partitions"
    - "low sensitivity functions from unambiguous certificates"
    - "polynomial bounds for decoupling with applications"
    - "almost quadratic gap between partition complexity and query communication complexity"
    - "analysis of boolean functions"
    - "a composition theorem for parity kill number"

Connected through hyperedge HE-3 (induced by source paper "sensitivity affine transforms and quantum communication complexity"):
  with members:
    - "sensitivity conjecture and log rank conjecture for functions with small alternating numbers"
    - "analysis of boolean functions"
    - "on the parity complexity measures of boolean functions"
    - "variations on the sensitivity conjecture"
    - "on quantum classical equivalence for composed communication problems"
    - "on the communication complexity of xor functions"
    - "communication complexity lower bounds by polynomials"

Connected through hyperedge HE-4 (induced by source paper "on the fourier entropy influence conjecture for extremal classes"):
  with members:
    - "analysis of boolean functions"
    - "a composition theorem for the fourier entropy influence conjecture"

Connected through hyperedge HE-5 (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"
    - "separations in query complexity based on pointer functions"
    - "smooth boolean functions are easy efficient algorithms for low sensitivity 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-6 (induced by source paper "on the sensitivity complexity of k uniform hypergraph properties"):
  with members:
    - "sensitivity conjecture and log rank conjecture for functions with small alternating numbers"
    - "evasiveness and the distribution of prime numbers"
    - "variations on the sensitivity conjecture"
    - "on the sensitivity of cyclically invariant boolean functions"
    - "lecture notes on evasiveness of graph properties"

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)