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: "hardness of robust graph isomorphism lasserre gaps and asymmetry of random graphs"
  Abstract: Building on work of Cai, F\"urer, and Immerman \cite{CFI92}, we show two hardness results for the Graph Isomorphism problem. First, we show that there are pairs of nonisomorphic $n$-vertex graphs $G$ and $H$ such that any sum-of-squares (SOS) proof of nonisomorphism requires degree $\Omega(n)$. In other words, we show an $\Omega(n)$-round integrality gap for the Lasserre SDP relaxation. In fact, we show this for pairs $G$ and $H$ which are not even $(1-10^{-14})$-isomorphic. (Here we say that two $n$-vertex, $m$-edge graphs $G$ and $H$ are $\alpha$-isomorphic if there is a bijection between their vertices which preserves at least $\alpha m$ edges.) Our second result is that under the {\sc R3XOR} Hypothesis \cite{Fei02} (and also any of a class of hypotheses which generalize the {\sc R3XOR} Hypothesis), the \emph{robust} Graph Isomorphism problem is hard. I.e.\ for every $\epsilon > 0$, there is no efficient algorithm which can distinguish graph pairs which are $(1-\epsilon)$-isomorphic from pairs which are not even $(1-\epsilon_0)$-isomorphic for some universal constant $\epsilon_0$. Along the way we prove a robust asymmetry result for random graphs and hypergraphs which may be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "a linear upper bound on the weisfeiler leman dimension of graphs of bounded genus"):
  with members:
    - "canonisation and definability for graphs of bounded rank width"
    - "a finite model theoretic view on propositional proof complexity"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "definable ellipsoid method sums of squares proofs and the isomorphism problem"
    - "the weisfeiler leman dimension of planar graphs is at most 3"
    - "on the combinatorial power of the weisfeiler lehman algorithm"
    - "semi supervised classification with graph convolutional networks"
    - "upper bounds on the quantifier depth for graph differentiation in first order logic"

Connected through hyperedge HE-2 (induced by source paper "on the equivalence between graph isomorphism testing and function approximation with gnns"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "on the universality of invariant networks"
    - "provably powerful graph networks"
    - "a comprehensive survey on graph neural networks"
    - "relational pooling for graph representations"
    - "universal approximations of invariant maps by neural networks"
    - "how powerful are graph neural networks"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-3 (induced by source paper "nearly efficient algorithms for the graph matching problem on correlated random graphs"):
  with members:
    - "seeded graph matching via large neighborhood statistics"
    - "the power of sum of squares for detecting hidden structures"
    - "exact alignment recovery for correlated erdos renyi graphs"
    - "an efficient reconciliation algorithm for social networks"
    - "de anonymizing social networks"

Connected through hyperedge HE-4 (induced by source paper "definable ellipsoid method sums of squares proofs and the isomorphism problem"):
  with members:
    - "a finite model theoretic view on propositional proof complexity"
    - "descriptive complexity of graph spectra"
    - "limitations of algebraic approaches to graph isomorphism testing"
    - "semidefinite programming and arithmetic circuit evaluation"

Connected through hyperedge HE-5 (induced by source paper "sum of squares lower bounds for refuting any csp"):
  with members:
    - "lower bounds for csp refutation by sdp hierarchies"
    - "the backtracking survey propagation algorithm for solving random k sat problems"
    - "strongly refuting random csps below the spectral threshold"
    - "how to refute a random csp"
    - "noisy tensor completion via the sum of squares hierarchy"
    - "complexity theoretic limitations on learning halfspaces"
    - "sum of squares lower bounds from pairwise independence"
    - "lower bounds on the size of semidefinite programming relaxations"

Connected through hyperedge HE-6 (induced by source paper "linear diophantine equations group csps and graph isomorphism"):
  with members:
    - "graph isomorphism in quasipolynomial time"

Connected through hyperedge HE-7 (induced by source paper "graph isomorphism in quasipolynomial time"):
  with members:
    - "graph isomorphism and the lasserre hierarchy"
    - "practical graph isomorphism ii"
    - "search space contraction in canonical labeling of graphs"

Connected through hyperedge HE-8 (induced by source paper "limitations of algebraic approaches to graph isomorphism testing"):
  with members:
    - "graph isomorphism and the lasserre hierarchy"
    - "pebble games and linear equations"

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)