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: "information cost tradeoffs for augmented index and streaming language recognition"
  Abstract: This paper makes three main contributions to the theory of communication complexity and stream computation. First, we present new bounds on the information complexity of AUGMENTED-INDEX. In contrast to analogous results for INDEX by Jain, Radhakrishnan and Sen [J. ACM, 2009], we have to overcome the significant technical challenge that protocols for AUGMENTED-INDEX may violate the "rectangle property" due to the inherent input sharing. Second, we use these bounds to resolve an open problem of Magniez, Mathieu and Nayak [STOC, 2010] that asked about the multi-pass complexity of recognizing Dyck languages. This results in a natural separation between the standard multi-pass model and the multi-pass model that permits reverse passes. Third, we present the first passive memory checkers that verify the interaction transcripts of priority queues, stacks, and double-ended queues. We obtain tight upper and lower bounds for these problems, thereby addressing an important sub-class of the memory checking framework of Blum et al. [Algorithmica, 1994].

Connected through hyperedge HE-1 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms"):
  with members:
    - "smaller cuts higher lower bounds"
    - "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "non monotone submodular maximization with nearly optimal adaptivity complexity"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "tight bounds on the round complexity of the distributed maximum coverage problem"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"

Connected through hyperedge HE-2 (induced by source paper "efficient exact paths for dyck and semi dyck labeled path reachability"):
  with members:
    - "powers of tensors and fast matrix multiplication"
    - "fast context free grammar parsing requires fast boolean matrix multiplication"
    - "all pairs shortest paths using bridging sets and rectangular matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "truly sub cubic algorithms for language edit distance and rna folding via fast bounded difference min plus product"):
  with members:
    - "clustered integer 3sum via additive combinatorics"
    - "hardness of rna folding problem with four symbols"
    - "if the current clique algorithms are optimal so is valiant s parser"
    - "powers of tensors and fast matrix multiplication"
    - "language edit distance maximum likelihood parsing of stochastic grammars faster algorithms connection to fundamental graph problems"
    - "an error correcting parser for context free grammars that takes less than cubic time"
    - "faster all pairs shortest paths via circuit complexity"
    - "streaming algorithms for recognizing nearly well parenthesized expressions"

Connected through hyperedge HE-4 (induced by source paper "a hybrid sampling scheme for triangle counting"):
  with members:
    - "approximately counting triangles in sublinear time"
    - "a second look at counting triangles in graph streams"
    - "how hard is counting triangles in the streaming model"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"
    - "colorful triangle counting and a mapreduce implementation"

Connected through hyperedge HE-5 (induced by source paper "improved graph sampling for triangle counting"):
  with members:
    - "approximately counting triangles in sublinear time"
    - "how hard is counting triangles in the streaming model"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"

Connected through hyperedge HE-6 (induced by source paper "incidence geometries and the pass complexity of semi streaming set cover"):
  with members:
    - "semi streaming set cover"
    - "analytical approach to parallel repetition"
    - "recognizing well parenthesized expressions in the streaming model"

Connected through hyperedge HE-7 (induced by source paper "unidirectional input output streaming complexity of reversal and sorting"):
  with members:
    - "on the value of multiple read write streams for data compression"
    - "streaming complexity of checking priority queues"
    - "data oblivious external memory algorithms for the compaction selection and sorting of outsourced data"
    - "validating xml documents in the streaming model with external memory"
    - "the space complexity of recognizing well parenthesized expressions in the streaming model the index function revisited"
    - "recognizing well parenthesized expressions in the streaming model"
    - "reversal complexity revisited"
    - "tight lower bounds for query processing on streaming and external memory data"

Connected through hyperedge HE-8 (induced by source paper "how hard is counting triangles in the streaming model"):
  with members:
    - "efficient triangle counting in large graphs via degree based vertex partitioning"

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)