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: "on the usefulness of predicates"
  Abstract: Motivated by the pervasiveness of strong inapproximability results for Max-CSPs, we introduce a relaxed notion of an approximate solution of a Max-CSP. In this relaxed version, loosely speaking, the algorithm is allowed to replace the constraints of an instance by some other (possibly real-valued) constraints, and then only needs to satisfy as many of the new constraints as possible. #R##N#To be more precise, we introduce the following notion of a predicate $P$ being \emph{useful} for a (real-valued) objective $Q$: given an almost satisfiable Max-$P$ instance, there is an algorithm that beats a random assignment on the corresponding Max-$Q$ instance applied to the same sets of literals. The standard notion of a nontrivial approximation algorithm for a Max-CSP with predicate $P$ is exactly the same as saying that $P$ is useful for $P$ itself. #R##N#We say that $P$ is useless if it is not useful for any $Q$. This turns out to be equivalent to the following pseudo-randomness property: given an almost satisfiable instance of Max-$P$ it is hard to find an assignment such that the induced distribution on $k$-bit strings defined by the instance is not essentially uniform. #R##N#Under the Unique Games Conjecture, we give a complete and simple characterization of useful Max-CSPs defined by a predicate: such a Max-CSP is useless if and only if there is a pairwise independent distribution supported on the satisfying assignments of the predicate. It is natural to also consider the case when no negations are allowed in the CSP instance, and we derive a similar complete characterization (under the UGC) there as well. #R##N#Finally, we also include some results and examples shedding additional light on the approximability of certain Max-CSPs.

Connected through hyperedge HE-1 (induced by source paper "algebraic approach to promise constraint satisfaction"):
  with members:
    - "topology is relevant in a dichotomy conjecture for infinite domain constraint satisfaction problems"
    - "promises make finite constraint satisfaction problems infinitary"
    - "dichotomy for symmetric boolean pcsps"
    - "an algorithmic blend of lps and ring equations for promise csps"
    - "improved inapproximability of rainbow coloring"
    - "super polylogarithmic hypergraph coloring hardness via low degree long codes"
    - "promise constraint satisfaction algebraic structure and a symmetric boolean dichotomy"
    - "a dichotomy theorem for nonuniform csps"

Connected through hyperedge HE-2 (induced by source paper "on the approximation resistance of balanced linear threshold functions"):
  with members:
    - "a dichotomy theorem for nonuniform csps"
    - "sum of squares lower bounds for refuting any csp"
    - "a characterization of approximation resistance for even k partite csps"
    - "a characterization of approximation resistance"
    - "approximation resistant predicates from pairwise independence"

Connected through hyperedge HE-3 (induced by source paper "robust algorithms with polynomial loss for near unanimity csps"):
  with members:
    - "a dichotomy theorem for nonuniform csps"
    - "the proof of csp dichotomy conjecture"
    - "weaker consistency notions for all the csps of bounded width"
    - "the complexity of general valued csps"
    - "n permutability and linear datalog implies symmetric datalog"

Connected through hyperedge HE-4 (induced by source paper "how to refute a random csp"):
  with members:
    - "complexity theoretic limitations on learning halfspaces"
    - "sum of squares lower bounds from pairwise independence"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "analysis of boolean functions"
    - "subsampled power iteration a unified algorithm for block models and planted csp s"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "from average case complexity to improper learning complexity"
    - "more data speeds up training time in learning halfspaces over sparse vectors"

Connected through hyperedge HE-5 (induced by source paper "approximate hypergraph coloring under low discrepancy and related promises"):
  with members:
    - "super polylogarithmic hypergraph coloring hardness via low degree long codes"
    - "constructive discrepancy minimization for convex sets"
    - "constructive discrepancy minimization by walking on the edges"
    - "constructive algorithms for discrepancy minimization"
    - "approximation resistant predicates from pairwise independence"
    - "approximate graph coloring by semidefinite programming"

Connected through hyperedge HE-6 (induced by source paper "refuting unique game conjecture"):
  with members:
    - "strengthened hardness for approximating minimum unique game and small set expansion"
    - "agnostic learning of monomials by halfspaces is hard"
    - "approximation resistant predicates from pairwise independence"

Connected through hyperedge HE-7 (induced by source paper "an o 1 84 k parameterized algorithm for the multiterminal cut problem"):
  with members:
    - "approximation algorithms for submodular multiway partition"
    - "minimum k way cut of bounded size is fixed parameter tractable"
    - "on multiway cut parameterized above lower bounds"
    - "multicut is fpt"
    - "fixed parameter tractability of multicut parameterized by the size of the cutset"

Connected through hyperedge HE-8 (induced by source paper "approximation resistance by disguising biased distributions"):
  with members:
    - "strengthened hardness for approximating minimum unique game and small set expansion"
    - "agnostic learning of monomials by halfspaces is hard"
    - "approximation resistant predicates from pairwise independence"

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)