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: "learning circuits with few negations"
  Abstract: Monotone Boolean functions, and the monotone Boolean circuits that compute them, have been intensively studied in complexity theory. In this paper we study the structure of Boolean functions in terms of the minimum number of negations in any circuit computing them, a complexity measure that interpolates between monotone functions and the class of all functions. We study this generalization of monotonicity from the vantage point of learning theory, giving near-matching upper and lower bounds on the uniform-distribution learnability of circuits in terms of the number of negations they contain. Our upper bounds are based on a new structural characterization of negation-limited circuits that extends a classical result of A. A. Markov. Our lower bounds, which employ Fourier-analytic tools from hardness amplification, give new results even for circuits with no negations (i.e. monotone functions).

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"
    - "degree and sensitivity tails of two distributions"
    - "sensitivity versus certificate complexity of boolean functions"
    - "analysis of boolean functions"
    - "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 "top down induction of decision trees rigorous guarantees and inherent limitations"):
  with members:
    - "on the optimality of trees generated by id3"
    - "beyond the low degree algorithm mixtures of subcubes and their applications"
    - "extractor based time space lower bounds for learning"
    - "hyperparameter optimization a spectral approach"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "analysis of boolean functions"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "the average sensitivity of an intersection of half spaces"

Connected through hyperedge HE-3 (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"
    - "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"
    - "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-4 (induced by source paper "testing k monotonicity"):
  with members:
    - "sensitivity conjecture and log rank conjecture for functions with small alternating numbers"
    - "analysis of boolean functions"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"
    - "new algorithms and lower bounds for monotonicity testing"
    - "an optimal lower bound for monotonicity testing over hypergrids"
    - "a o n monotonicity tester for boolean functions over the hypercube"
    - "optimal bounds for monotonicity and lipschitz testing over hypercubes and hypergrids"
    - "active property testing"

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)