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 direct sum theorem in communication complexity via message compression"
  Abstract: We prove lower bounds for the direct sum problem for two-party bounded error randomised multiple-round communication protocols. Our proofs use the notion of information cost of a protocol, as defined by Chakrabarti, Shi, Wirth and Yao and refined further by Bar-Yossef, Jayram, Kumar and Sivakumar. Our main technical result is a `compression' theorem saying that, for any probability distribution $\mu$ over the inputs, a $k$-round private coin bounded error protocol for a function $f$ with information cost $c$ can be converted into a $k$-round deterministic protocol for $f$ with bounded distributional error and communication cost $O(kc)$. We prove this result using a substate theorem about relative entropy and a rejection sampling argument. Our direct sum result follows from this `compression' result via elementary information theoretic arguments. #R##N#We also consider the direct sum problem in quantum communication. Using a probabilistic argument, we show that messages cannot be compressed in this manner even if they carry small information. Hence, new techniques may be necessary to tackle the direct sum problem in quantum communication.

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 "round complexity of common randomness generation the amortized setting"):
  with members:
    - "communication rounds tradeoffs for common randomness and secret key generation"
    - "resource efficient common randomness and secret key schemes"
    - "the power of shared randomness in uncertain communication"
    - "secret key generation with limited interaction"
    - "on the role of shared randomness in simultaneous communication"
    - "secret key generation with one communicator and a one shot converse via hypercontractivity"
    - "communication complexity of permutation invariant functions"
    - "communication with imperfectly shared randomness"

Connected through hyperedge HE-3 (induced by source paper "on estimating maximum matching size in graph streams"):
  with members:
    - "on approximating functions of the singular values in a stream"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "maximum matching in turnstile streams"
    - "on the communication complexity of linear algebraic problems in the message passing model"
    - "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"
    - "better bounds for matchings in the streaming model"
    - "superlinear lower bounds for multipass graph processing"
    - "maximum matching in semi streaming with few passes"

Connected through hyperedge HE-4 (induced by source paper "a unified approach to source and message compression"):
  with members:
    - "a one shot achievability result for quantum state redistribution"
    - "wiretap channels with random states non causally available at the encoder"
    - "simple one shot bounds for various source coding problems using smooth renyi quantities"
    - "on the dispersions of three network information theory problems"
    - "a technique for deriving one shot achievability results in network information theory"
    - "distributed channel synthesis"
    - "fixed length lossy compression in the finite blocklength regime"
    - "information equals amortized communication"

Connected through hyperedge HE-5 (induced by source paper "strong functional representation lemma and applications to coding theorems"):
  with members:
    - "the likelihood encoder for lossy compression"
    - "distributed simulation of continuous random variables"
    - "one shot mutual covering lemma and marton s inner bound with a common message"
    - "resolvability in e γ with applications to lossy compression and wiretap channels"
    - "exact common information"
    - "variable length compression allowing errors"
    - "a technique for deriving one shot achievability results in network information theory"
    - "distributed channel synthesis"

Connected through hyperedge HE-6 (induced by source paper "simulation theorems via pseudorandom properties"):
  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"
    - "improved direct product theorems for randomized query complexity"
    - "strong direct product theorems for quantum communication and query complexity"

Connected through hyperedge HE-7 (induced by source paper "multi party protocols information complexity and privacy"):
  with members:
    - "the range of topological effects on communication"
    - "tribes is hard in the message passing model"
    - "tight bounds for set disjointness in the message passing model"
    - "information equals amortized communication"
    - "lower bounds for number in hand multiparty communication complexity made easy"
    - "distributed verification and hardness of distributed approximation"
    - "a strong direct product theorem for disjointness"

Connected through hyperedge HE-8 (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"
    - "a discrepancy lower bound for information complexity"
    - "the partition bound for classical communication complexity and query complexity"

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)