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: "a discrepancy lower bound for information complexity"
  Abstract: This paper provides the first general technique for proving information lower bounds on two-party unbounded-rounds communication problems. We show that the discrepancy lower bound, which applies to randomized communication complexity, also applies to information complexity. More precisely, if the discrepancy of a two-party function $f$ with respect to a distribution $\mu$ is $Disc_\mu f$, then any two party randomized protocol computing $f$ must reveal at least $\Omega(\log (1/Disc_\mu f))$ bits of information to the participants. As a corollary, we obtain that any two-party protocol for computing a random function on $\{0,1\}^n\times\{0,1\}^n$ must reveal $\Omega(n)$ bits of information to the participants. #R##N#In addition, we prove that the discrepancy of the Greater-Than function is $\Omega(1/\sqrt{n})$, which provides an alternative proof to the recent proof of Viola \cite{Viola11} of the $\Omega(\log n)$ lower bound on the communication complexity of this well-studied function and, combined with our main result, proves the tight $\Omega(\log n)$ lower bound on its information complexity. #R##N#The proof of our main result develops a new simulation procedure that may be of an independent interest. In a very recent breakthrough work of Kerenidis et al. \cite{kerenidis2012lower}, this simulation procedure was the main building block for proving that almost all known lower bound techniques for communication complexity (and not just discrepancy) apply to information complexity.

Connected through hyperedge HE-1 (induced by source paper "detecting patterns can be hard circuit lower bounds for the string matching problem"):
  with members:
    - "deleting and testing forbidden patterns in multi dimensional arrays"
    - "the power of depth for feedforward neural networks"
    - "the optimal sample complexity of pac learning"
    - "super linear gate and super quadratic wire lower bounds for depth two and depth three threshold circuits"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"

Connected through hyperedge HE-2 (induced by source paper "quantum communication query tradeoffs"):
  with members:
    - "query to communication lifting for bpp"
    - "lifting randomized query complexity to randomized communication complexity"
    - "an approximation algorithm for approximation rank"
    - "communication complexity lower bounds by polynomials"

Connected through hyperedge HE-3 (induced by source paper "r enyi information complexity and an information theoretic characterization of the partition bound"):
  with members:
    - "tension bounds for information complexity"
    - "lower bounds on information complexity via zero communication protocols and applications"
    - "information complexity versus corruption and applications to orthogonality and gap hamming"
    - "information equals amortized communication"
    - "the partition bound for classical communication complexity and query complexity"
    - "a direct sum theorem in communication complexity via message compression"

Connected through hyperedge HE-4 (induced by source paper "information complexity and the quest for interactive compression a survey"):
  with members:
    - "a strong direct product theorem in terms of the smooth rectangle bound"
    - "lower bounds on information complexity via zero communication protocols and applications"
    - "information equals amortized communication"
    - "strong direct product theorems for quantum communication and query complexity"
    - "a strong direct product theorem for disjointness"

Connected through hyperedge HE-5 (induced by source paper "tension bounds for information complexity"):
  with members:
    - "assisted common information with an application to secure two party sampling"
    - "on the communication complexity of secure computation"
    - "lower bounds on information complexity via zero communication protocols and applications"
    - "information complexity versus corruption and applications to orthogonality and gap hamming"
    - "information equals amortized communication"
    - "a direct sum theorem in communication complexity via message compression"

Connected through hyperedge HE-6 (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"
    - "tight bounds for distributed functional monitoring"
    - "an optimal lower bound on the communication complexity of gap hamming distance"
    - "the space complexity of recognizing well parenthesized expressions in the streaming model the index function revisited"
    - "quantum one way communication is exponentially stronger than classical communication"
    - "the partition bound for classical communication complexity and query complexity"

Connected through hyperedge HE-7 (induced by source paper "superlinear lower bounds for multipass graph processing"):
  with members:
    - "better bounds for matchings in the streaming model"
    - "a direct product theorem for bounded round public coin randomized communication complexity"
    - "lower bounds on information complexity via zero communication protocols and applications"
    - "information complexity versus corruption and applications to orthogonality and gap hamming"
    - "maximum matching in semi streaming with few passes"
    - "information equals amortized communication"
    - "improved approximation guarantees for weighted matching in the semi streaming model"
    - "linear programming in the semi streaming model with application to the maximum matching problem"

Connected through hyperedge HE-8 (induced by source paper "a strong direct product theorem in terms of the smooth rectangle bound"):
  with members:
    - "a strong direct product theorem for the tribes function via the smooth rectangle bound"
    - "lower bounds on information complexity via zero communication protocols and applications"
    - "information equals amortized communication"
    - "an optimal lower bound on the communication complexity of gap hamming distance"
    - "strong direct product theorems for quantum communication and query complexity"
    - "the partition bound for classical communication complexity and query complexity"
    - "a strong direct product theorem for disjointness"
    - "a direct sum theorem in communication complexity via message compression"

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)