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: "annotations for sparse data streams"
  Abstract: Motivated by cloud computing, a number of recent works have studied annotated data streams and variants thereof. In this setting, a computationally weak verifier (cloud user), lacking the resources to store and manipulate his massive input locally, accesses a powerful but untrusted prover (cloud service). The verifier must work within the restrictive data streaming paradigm. The prover, who can annotate the data stream as it is read, must not just supply the answer but also convince the verifier of its correctness. Ideally, both the amount of annotation and the space used by the verifier should be sublinear in the relevant input size parameters. #R##N#A rich theory of such algorithms -- which we call schemes -- has emerged. Prior work has shown how to leverage the prover's power to efficiently solve problems that have no non-trivial standard data stream algorithms. However, while optimal schemes are now known for several basic problems, such optimality holds only for streams whose length is commensurate with the size of the data universe. In contrast, many real-world datasets are relatively sparse, including graphs that contain only O(n^2) edges, and IP traffic streams that contain much fewer than the total number of possible IP addresses, 2^128 in IPv6. #R##N#We design the first schemes that allow both the annotation and the space usage to be sublinear in the total number of stream updates rather than the size of the data universe. We solve significant problems, including variations of INDEX, SET-DISJOINTNESS, and FREQUENCY-MOMENTS, plus several natural problems on graphs. On the other hand, we give a new lower bound that, for the first time, rules out smooth tradeoffs between annotation and space usage for a specific problem. Our technique brings out new nuances in Merlin-Arthur communication complexity models, and provides a separation between online versions of the MA and AMA models.

Connected through hyperedge HE-1 (induced by source paper "on szk and pp"):
  with members:
    - "adaptivity vs postselection"
    - "dual polynomials for collision and element distinctness"
    - "verifying computations with streaming interactive proofs"
    - "on arthur merlin games in communication complexity"
    - "bounded independence fools halfspaces"

Connected through hyperedge HE-2 (induced by source paper "streaming verification in data analysis"):
  with members:
    - "an improved interactive streaming algorithm for the distinct elements problem"
    - "arthur merlin streaming complexity"
    - "practical verified computation with streaming interactive proofs"
    - "verifying computations with streaming interactive proofs"
    - "on arthur merlin games in communication complexity"
    - "streaming graph computations with a helpful advisor"

Connected through hyperedge HE-3 (induced by source paper "semi streaming algorithms for annotated graph streams"):
  with members:
    - "an improved interactive streaming algorithm for the distinct elements problem"
    - "arthur merlin streaming complexity"
    - "time optimal interactive proofs for circuit evaluation"
    - "single pass sparsification in the streaming model with edge deletions"
    - "practical verified computation with streaming interactive proofs"
    - "verifying computations with streaming interactive proofs"
    - "invertible bloom lookup tables"
    - "streaming graph computations with a helpful advisor"

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)