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: "fast enumeration of combinatorial objects"
  Abstract: The problem of ranking can be described as follows. We have a set of combinatorial objects $S$, such as, say, the k-subsets of n things, and we can imagine that they have been arranged in some list, say lexicographically, and we want to have a fast method for obtaining the rank of a given object in the list. This problem is widely known in Combinatorial Analysis, Computer Science and Information Theory. Ranking is closely connected with the hashing problem, especially with perfect hashing and with generating of random combinatorial objects. In Information Theory the ranking problem is closely connected with so-called enumerative encoding, which may be described as follows: there is a set of words $S$ and an enumerative code has to one-to-one encode every $s \in S$ by a binary word $code(s)$. The length of the $code(s)$ must be the same for all $s \in S$. Clearly, $|code (s)|\geq \log |S|$. (Here and below $\log x=\log_{2}x)$.) The suggested method allows the exponential growth of the speed of encoding and decoding for all combinatorial problems of enumeration which are considered, including the enumeration of permutations, compositions and others.

Connected through hyperedge HE-1 (induced by source paper "asymptotically optimum perfect universal steganography of finite memoryless sources"):
  with members:
    - "perfectly secure steganography capacity error exponents and code constructions"

Connected through hyperedge HE-2 (induced by source paper "optimal algorithms for universal random number generation from finite memory sources"):
  with members:
    - "a universal scheme for transforming binary algorithms to generate random bits from loaded dice"
    - "efficient generation of random bits from finite state markov chains"

Connected through hyperedge HE-3 (induced by source paper "constructing perfect steganographic systems"):
  with members:
    - "efficient generation of random bits from finite state markov chains"
    - "using kolmogorov complexity for understanding some limitations on steganography"
    - "minimum description length induction bayesianism and kolmogorov complexity"

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)