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: "benchmark graphs for practical graph isomorphism"
  Abstract: The state-of-the-art solvers for the graph isomorphism problem can readily solve generic instances with tens of thousands of vertices. Indeed, experiments show that on inputs without particular combinatorial structure the algorithms scale almost linearly. In fact, it is non-trivial to create challenging instances for such solvers and the number of difficult benchmark graphs available is quite limited. We describe a construction to efficiently generate small instances for the graph isomorphism problem that are difficult or even infeasible for said solvers. Up to this point the only other available instances posing challenges for isomorphism solvers were certain incidence structures of combinatorial objects (such as projective planes, Hadamard matrices, Latin squares, etc.). Experiments show that starting from 1500 vertices our new instances are several orders of magnitude more difficult on comparable input sizes. More importantly, our method is generic and efficient in the sense that one can quickly create many isomorphism instances on a desired number of vertices. In contrast to this, said combinatorial objects are rare and difficult to generate and with the new construction it is possible to generate an abundance of instances of arbitrary size. Our construction hinges on the multipedes of Gurevich and Shelah and the Cai-F\"{u}rer-Immerman gadgets that realize a certain abelian automorphism group and have repeatedly played a role in the context of graph isomorphism. Exploring limits of such constructions, we also explain that there are group theoretic obstructions to generalizing the construction with non-abelian gadgets.

Connected through hyperedge HE-1 (induced by source paper "local wl invariance and hidden shades of regularity"):
  with members:
    - "identifiability of graphs with small color classes by the weisfeiler leman algorithm"
    - "on the weisfeiler leman dimension of fractional packing"
    - "a survey on graph kernels"
    - "on weisfeiler leman invariance subgraph counts and related graph properties"
    - "homomorphisms are a good basis for counting small subgraphs"
    - "on the combinatorial power of the weisfeiler lehman algorithm"
    - "graph isomorphism in quasipolynomial time"

Connected through hyperedge HE-2 (induced by source paper "learning to sample hard instances for graph algorithms"):
  with members:
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "constrained graph variational autoencoders for molecule design"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "prioritized experience replay"

Connected through hyperedge HE-3 (induced by source paper "identifiability of graphs with small color classes by the weisfeiler leman algorithm"):
  with members:
    - "constructing hard examples for graph isomorphism"
    - "on the combinatorial power of the weisfeiler lehman algorithm"
    - "graph isomorphism in quasipolynomial time"
    - "graphs identified by logics with counting"
    - "powers of tensors and fast matrix multiplication"
    - "fast matrix rank algorithms and applications"

Connected through hyperedge HE-4 (induced by source paper "constructing hard examples for graph isomorphism"):
  with members:
    - "definable inapproximability new challenges for duplicator"
    - "an exponential lower bound for individualization refinement algorithms for graph isomorphism"
    - "the pebbling comonad in finite model theory"
    - "graph isomorphism in quasipolynomial time"
    - "lower bounds for existential pebble games and k consistency tests"
    - "practical graph isomorphism ii"
    - "clause learning algorithms with many restarts and bounded width resolution"

Connected through hyperedge HE-5 (induced by source paper "robust exponential worst cases for divide et impera algorithms for parity games"):
  with members:
    - "an ordered approach to solving parity games in quasi polynomial time and quasi linear space"
    - "succinct progress measures for solving parity games"
    - "reasoning about strategies on the satisfiability problem"
    - "a delayed promotion policy for parity games"
    - "reasoning about strategies on the model checking problem"
    - "generalized mean payoff and energy games"

Connected through hyperedge HE-6 (induced by source paper "an exponential lower bound for individualization refinement algorithms for graph isomorphism"):
  with members:
    - "practical graph isomorphism ii"
    - "conauto 2 0 fast isomorphism testing and automorphism group computation"

Connected through hyperedge HE-7 (induced by source paper "a generic framework for engineering graph canonization algorithms"):
  with members:
    - "practical graph isomorphism ii"

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.DS(Data Structures and Algorithms)