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: "understanding space in proof complexity separations and trade offs via substitutions"
  Abstract: For current state-of-the-art DPLL SAT-solvers the two main bottlenecks are the amounts of time and memory used. In proof complexity, these resources correspond to the length and space of resolution proofs. There has been a long line of research investigating these proof complexity measures, but while strong results have been established for length, our understanding of space and how it relates to length has remained quite poor. In particular, the question whether resolution proofs can be optimized for length and space simultaneously, or whether there are trade-offs between these two measures, has remained essentially open. #R##N#In this paper, we remedy this situation by proving a host of length-space trade-off results for resolution. Our collection of trade-offs cover almost the whole range of values for the space complexity of formulas, and most of the trade-offs are superpolynomial or even exponential and essentially tight. Using similar techniques, we show that these trade-offs in fact extend to the exponentially stronger k-DNF resolution proof systems, which operate with formulas in disjunctive normal form with terms of bounded arity k. We also answer the open question whether the k-DNF resolution systems form a strict hierarchy with respect to space in the affirmative. #R##N#Our key technical contribution is the following, somewhat surprising, theorem: Any CNF formula F can be transformed by simple variable substitution into a new formula F' such that if F has the right properties, F' can be proven in essentially the same length as F, whereas on the other hand the minimal number of lines one needs to keep in memory simultaneously in any proof of F' is lower-bounded by the minimal number of variables needed simultaneously in any proof of F. Applying this theorem to so-called pebbling formulas defined in terms of pebble games on directed acyclic graphs, we obtain our results.

Connected through hyperedge HE-1 (induced by source paper "near optimal lower bounds on quantifier depth and weisfeiler leman refinement steps"):
  with members:
    - "upper bounds on the quantifier depth for graph differentiation in first order logic"
    - "graph isomorphism in quasipolynomial time"
    - "a generalized method for proving polynomial calculus degree lower bounds"
    - "universal covers color refinement and two variable counting logic lower bounds for the depth"
    - "the propagation depth of local consistency"
    - "pebble games proof complexity and time space trade offs"

Connected through hyperedge HE-2 (induced by source paper "space proof complexity for random 3 cnfs"):
  with members:
    - "space proof complexity for random 3 cnfs via a 2 epsilon hall s theorem"
    - "pebble games proof complexity and time space trade offs"

Connected through hyperedge HE-3 (induced by source paper "narrow proofs may be maximally long"):
  with members:
    - "a generalized method for proving polynomial calculus degree lower bounds"
    - "tight size degree bounds for sums of squares proofs"
    - "communication lower bounds via critical block sensitivity"
    - "on the complexity of finding narrow proofs"
    - "hypercontractivity sum of squares proofs and their applications"
    - "approximability and proof complexity"
    - "clause learning algorithms with many restarts and bounded width resolution"

Connected through hyperedge HE-4 (induced by source paper "from small space to small width in resolution"):
  with members:
    - "narrow proofs may be maximally long"
    - "pebble games proof complexity and time space trade offs"

Connected through hyperedge HE-5 (induced by source paper "pebble games proof complexity and time space trade offs"):
  with members:
    - "clause learning algorithms with many restarts and bounded width resolution"
    - "on the relative strength of pebbling and resolution"
    - "towards an optimal separation of space and length in resolution"
    - "towards understanding and harnessing the potential of clause learning"

Connected through hyperedge HE-6 (induced by source paper "hardness measures and resolution lower bounds"):
  with members:
    - "on sat representations of xor constraints"
    - "trading inference effort versus size in cnf knowledge compilation"
    - "towards a theory of good sat representations"
    - "circuit complexity and decompositions of global constraints"

Connected through hyperedge HE-7 (induced by source paper "on the relative strength of pebbling and resolution"):
  with members:
    - "towards an optimal separation of space and length in resolution"

Connected through hyperedge HE-8 (induced by source paper "on minimal unsatisfiability and time space trade offs for k dnf resolution"):
  with members:
    - "towards an optimal separation of space and length in resolution"

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)