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: "the space complexity of recognizing well parenthesized expressions in the streaming model the index function revisited"
  Abstract: We show an Omega(sqrt{n}/T) lower bound for the space required by any unidirectional constant-error randomized T-pass streaming algorithm that recognizes whether an expression over two types of parenthesis is well-parenthesized. This proves a conjecture due to Magniez, Mathieu, and Nayak (2009) and rigorously establishes that bidirectional streams are exponentially more efficient in space usage as compared with unidirectional ones. We obtain the lower bound by establishing the minimum amount of information that is necessarily revealed by the players about their respective inputs in a two-party communication protocol for a variant of the Index function, namely Augmented Index. The information cost trade-off is obtained by a novel application of the conceptually simple and familiar ideas such as average encoding and the cut-and-paste property of randomized protocols. #R##N#Motivated by recent examples of exponential savings in space by streaming quantum algorithms, we also study quantum protocols for Augmented Index. Defining an appropriate notion of information cost for quantum protocols involves a delicate balancing act between its applicability and the ease with which we can analyze it. We define a notion of quantum information cost which reflects some of the non-intuitive properties of quantum information and give a trade-off for this notion. While this trade-off demonstrates the strength of our proof techniques, it does not lead to a space lower bound for checking parentheses. We leave such an implication for quantum streaming algorithms as an intriguing open question.

Connected through hyperedge HE-1 (induced by source paper "sliding window property testing for regular languages"):
  with members:
    - "visibly pushdown languages over sliding windows"
    - "randomized sliding window algorithms for regular languages"
    - "automata theory on sliding windows"
    - "streaming pattern matching with d wildcards"
    - "approximate hamming distance in a stream"
    - "dictionary matching in a stream"
    - "streaming property testing of visibly pushdown languages"
    - "the k mismatch problem revisited"

Connected through hyperedge HE-2 (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"
    - "information cost tradeoffs for augmented index and streaming language recognition"
    - "validating xml documents in the streaming model with external memory"
    - "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-3 (induced by source paper "lower bounds on information complexity via zero communication protocols and applications"):
  with members:
    - "a strong direct product theorem in terms of the smooth rectangle bound"
    - "information complexity versus corruption and applications to orthogonality and gap hamming"
    - "information equals amortized communication"
    - "a discrepancy lower bound for information complexity"
    - "tight bounds for distributed functional monitoring"
    - "an optimal lower bound on the communication complexity of gap hamming distance"
    - "quantum one way communication is exponentially stronger than classical communication"
    - "the partition bound for classical communication complexity and query complexity"

Connected through hyperedge HE-4 (induced by source paper "streaming complexity of checking priority queues"):
  with members:
    - "lower bounds on information complexity via zero communication protocols and applications"
    - "information equals amortized communication"
    - "information cost tradeoffs for augmented index and streaming language recognition"
    - "validating xml documents in the streaming model with external memory"
    - "recognizing well parenthesized expressions in the streaming model"

Connected through hyperedge HE-5 (induced by source paper "information cost tradeoffs for augmented index and streaming language recognition"):
  with members:
    - "lower bounds for sparse recovery"
    - "unifying the landscape of cell probe lower bounds"
    - "recognizing well parenthesized expressions in the streaming model"

Connected through hyperedge HE-6 (induced by source paper "validating xml documents in the streaming model with external memory"):
  with members:
    - "recognizing well parenthesized expressions in the streaming model"
    - "randomized computations on large data sets tight lower bounds"
    - "tight lower bounds for query processing on streaming and external memory data"

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)