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: "the complexity of phylogeny constraint satisfaction problems"
  Abstract: We systematically study the computational complexity of a broad class of computational problems in phylogenetic reconstruction. The class contains for example the rooted triple consistency problem, forbidden subtree problems, the quartet consistency problem, and many other problems studied in the bioinformatics literature. The studied problems can be described as \emph{constraint satisfaction problems} where the constraints have a first-order definition over the rooted triple relation. We show that every such phylogeny problem can be solved in polynomial time or is NP-complete. On the algorithmic side, we generalize a well-known polynomial-time algorithm of Aho, Sagiv, Szymanski, and Ullman for the rooted triple consistency problem. Our algorithm repeatedly solves linear equation systems to construct a solution in polynomial time. We then show that every phylogeny problem that cannot be solved by our algorithm is NP-complete. Our classification establishes a dichotomy for a large class of infinite structures that we believe is of independent interest in universal algebra, model theory, and topology. The proof of our main result combines results and techniques from various research areas: a recent classification of the model-complete cores of the reducts of the homogeneous binary branching C-relation, Leeb's Ramsey theorem for rooted trees, and universal algebra.

Connected through hyperedge HE-1 (induced by source paper "learning variable ordering heuristics for solving constraint satisfaction problems"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "how powerful are graph neural networks"
    - "neural message passing for quantum chemistry"
    - "learning combinatorial optimization algorithms over graphs"
    - "time limits in reinforcement learning"
    - "neural combinatorial optimization with reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "topology is relevant in a dichotomy conjecture for infinite domain constraint satisfaction problems"):
  with members:
    - "a universal algebraic proof of the complexity dichotomy for monotone monadic snp"
    - "algebraic approach to promise constraint satisfaction"
    - "a dichotomy theorem for nonuniform csps"
    - "the proof of csp dichotomy conjecture"
    - "constraint satisfaction problems for reducts of homogeneous graphs"
    - "the algebraic dichotomy conjecture for infinite domain constraint satisfaction problems"
    - "a complexity dichotomy for poset constraint satisfaction"
    - "equations in oligomorphic clones and the constraint satisfaction problem for omega categorical structures"

Connected through hyperedge HE-3 (induced by source paper "finite relation algebras with normal representations"):
  with members:
    - "a universal algebraic proof of the complexity dichotomy for monotone monadic snp"
    - "a dichotomy theorem for nonuniform csps"
    - "the proof of csp dichotomy conjecture"
    - "constraint satisfaction problems for reducts of homogeneous graphs"
    - "the algebraic dichotomy conjecture for infinite domain constraint satisfaction problems"
    - "a complexity dichotomy for poset constraint satisfaction"
    - "on the scope of the universal algebraic approach to constraint satisfaction"
    - "cores of countably categorical structures"

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)