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: "size of sets with small sensitivity a generalization of simon s lemma"
  Abstract: We study the structure of sets \(S\subseteq \{0, 1\}^n\) with small sensitivity. The well-known Simon’s lemma says that any \(S\subseteq \{0, 1\}^n\) of sensitivity \(s\) must be of size at least \(2^{n-s}\). This result has been useful for proving lower bounds on the sensitivity of Boolean functions, with applications to the theory of parallel computing and the “sensitivity vs. block sensitivity” conjecture.

Connected through hyperedge HE-1 (induced by source paper "sensitivity conjecture and log rank conjecture for functions with small alternating numbers"):
  with members:
    - "smooth boolean functions are easy efficient algorithms for low sensitivity functions"
    - "sensitivity versus certificate complexity of boolean functions"
    - "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-2 (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"
    - "smooth boolean functions are easy efficient algorithms for low sensitivity functions"
    - "sensitivity versus certificate complexity of boolean functions"
    - "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-3 (induced by source paper "degree and sensitivity tails of two distributions"):
  with members:
    - "smooth boolean functions are easy efficient algorithms for low sensitivity functions"
    - "tighter relations between sensitivity and other complexity measures"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-4 (induced by source paper "smooth boolean functions are easy efficient algorithms for low sensitivity functions"):
  with members:
    - "sensitivity versus certificate complexity of boolean functions"
    - "a tight lower bound on certificate complexity in terms of block sensitivity and sensitivity"
    - "analysis of boolean functions"
    - "tighter relations between sensitivity and other complexity measures"
    - "new separation between s f and bs f"
    - "variations on the sensitivity conjecture"

Connected through hyperedge HE-5 (induced by source paper "sensitivity versus certificate complexity of boolean functions"):
  with members:
    - "tighter relations between sensitivity and other complexity measures"
    - "new separation between s f and bs f"
    - "sensitivity versus block sensitivity of boolean functions"

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)