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: "randomization can be as helpful as a glimpse of the future in online computation"
  Abstract: We provide simple but surprisingly useful direct product theorems for proving lower bounds on online algorithms with a limited amount of advice about the future. As a consequence, we are able to translate decades of research on randomized online algorithms to the advice complexity model. Doing so improves significantly on the previous best advice complexity lower bounds for many online problems, or provides the first known lower bounds. For example, if $n$ is the number of requests, we show that: #R##N#(1) A paging algorithm needs $\Omega(n)$ bits of advice to achieve a competitive ratio better than $H_k=\Omega(\log k)$, where $k$ is the cache size. Previously, it was only known that $\Omega(n)$ bits of advice were necessary to achieve a constant competitive ratio smaller than $5/4$. #R##N#(2) Every $O(n^{1-\varepsilon})$-competitive vertex coloring algorithm must use $\Omega(n\log n)$ bits of advice. Previously, it was only known that $\Omega(n\log n)$ bits of advice were necessary to be optimal. #R##N#For certain online problems, including the MTS, $k$-server, paging, list update, and dynamic binary search tree problem, our results imply that randomization and sublinear advice are equally powerful (if the underlying metric space or node set is finite). This means that several long-standing open questions regarding randomized online algorithms can be equivalently stated as questions regarding online algorithms with sublinear advice. For example, we show that there exists a deterministic $O(\log k)$-competitive $k$-server algorithm with advice complexity $o(n)$ if and only if there exists a randomized $O(\log k)$-competitive $k$-server algorithm without advice. #R##N#Technically, our main direct product theorem is obtained by extending an information theoretical lower bound technique due to Emek, Fraigniaud, Korman, and Ros\'en [ICALP'09].

Connected through hyperedge HE-1 (induced by source paper "tight bounds for online edge coloring"):
  with members:
    - "online matching with general arrivals"
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "how to match when all vertices arrive online"
    - "an economic based analysis of ranking for online bipartite matching"
    - "tighter bounds for online bipartite matching"
    - "deterministic distributed edge coloring with fewer colors"
    - "dynamic algorithms for graph coloring"
    - "deterministic distributed edge coloring via hypergraph maximal matching"

Connected through hyperedge HE-2 (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"
    - "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"
    - "online bin packing with advice"

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"
    - "online algorithms with advice for bin packing and scheduling problems"
    - "online bin packing with advice"

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"
    - "greedy matching guarantees and limitations"
    - "online bin packing with advice"

Connected through hyperedge HE-5 (induced by source paper "weighted online problems with advice"):
  with members:
    - "on the power of advice and randomization for online bipartite matching"
    - "online bounded analysis"
    - "online algorithms with advice for bin packing and scheduling problems"
    - "advice complexity of the online induced subgraph problem"
    - "online makespan minimization with parallel schedules"

Connected through hyperedge HE-6 (induced by source paper "on the limitations of deterministic de randomizations for online bipartite matching and max sat"):
  with members:
    - "on the power of advice and randomization for online bipartite matching"
    - "deterministic algorithms for submodular maximization problems"
    - "greedy matching guarantees and limitations"
    - "online vertex weighted bipartite matching and single bid budgeted allocations"

Connected through hyperedge HE-7 (induced by source paper "advice complexity of the online search problem"):
  with members:
    - "on advice complexity of the k server problem under sparse metrics"
    - "a comparison of performance measures via online search"

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)