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: "online bin packing with advice"
  Abstract: We consider the online bin packing problem under the advice complexity model where the 'online constraint' is relaxed and an algorithm receives partial information about the future requests. We provide tight upper and lower bounds for the amount of advice an algorithm needs to achieve an optimal packing. We also introduce an algorithm that, when provided with log n + o(log n) bits of advice, achieves a competitive ratio of 3/2 for the general problem. This algorithm is simple and is expected to find real-world applications. We introduce another algorithm that receives 2n + o(n) bits of advice and achieves a competitive ratio of 4/3 + {\epsilon}. Finally, we provide a lower bound argument that implies that advice of linear size is required for an algorithm to achieve a competitive ratio better than 9/8.

Connected through hyperedge HE-1 (induced by source paper "online bin covering with advice"):
  with members:
    - "a new lower bound for classic online bin packing"
    - "a new and improved algorithm for online bin packing"
    - "a simple ptas for the dual bin packing problem and advice complexity of its online version"
    - "on the power of advice and randomization for online bipartite matching"
    - "randomization can be as helpful as a glimpse of the future in online computation"
    - "a logarithmic additive integrality gap for bin packing"
    - "online algorithms with advice for bin packing and scheduling problems"
    - "approximating bin packing within o log opt log log opt bins"

Connected through hyperedge HE-2 (induced by source paper "parallel online algorithms for the bin packing problem"):
  with members:
    - "a new lower bound for classic online bin packing"
    - "a new and improved algorithm for online bin packing"
    - "beating the harmonic lower bound for online bin packing"
    - "online algorithms with advice for bin packing and scheduling problems"
    - "on the list update problem with advice"
    - "online makespan minimization with parallel schedules"

Connected through hyperedge HE-3 (induced by source paper "online computation with untrusted advice"):
  with members:
    - "competitive caching with machine learned advice"
    - "a new lower bound for classic online bin packing"
    - "a new and improved algorithm for online bin packing"
    - "randomization can be as helpful as a glimpse of the future in online computation"
    - "online algorithms with advice for bin packing and scheduling problems"

Connected through hyperedge HE-4 (induced by source paper "advice complexity of priority algorithms"):
  with members:
    - "on the limitations of deterministic de randomizations for online bipartite matching and max sat"
    - "on the power of advice and randomization for online bipartite matching"
    - "randomization can be as helpful as a glimpse of the future in online computation"
    - "greedy matching guarantees and limitations"

Connected through hyperedge HE-5 (induced by source paper "lower bounds for several online variants of bin packing"):
  with members:
    - "a new lower bound for classic online bin packing"
    - "a new and improved algorithm for online bin packing"
    - "improved lower bounds for online hypercube packing"
    - "online bin packing with cardinality constraints resolved"

Connected through hyperedge HE-6 (induced by source paper "a simple ptas for the dual bin packing problem and advice complexity of its online version"):
  with members:
    - "on conceptually simple algorithms for variants of online bipartite matching"
    - "on the power of advice and randomization for online bipartite matching"
    - "the advice complexity of a class of hard online problems"

Connected through hyperedge HE-7 (induced by source paper "towards bin packing preliminary problem survey models with multiset estimates"):
  with members:
    - "online colored bin packing"
    - "packing scheduling and covering problems in a game theoretic perspective"
    - "minimization strategies for maximally parallel multiset rewriting systems"
    - "bin completion algorithms for multicontainer packing knapsack and covering problems"

Connected through hyperedge HE-8 (induced by source paper "randomization can be as helpful as a glimpse of the future in online computation"):
  with members:
    - "on the power of advice and randomization for online bipartite matching"
    - "optimal online edge coloring of planar graphs with advice"
    - "online multi coloring with advice"
    - "on the list update problem with advice"
    - "online makespan minimization with parallel schedules"
    - "a competitive ratio approximation scheme for the k server problem in fixed finite metrics"
    - "on advice complexity of the k server problem under sparse metrics"
    - "a new approach to online scheduling approximating the optimal competitive ratio"

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)