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: "an optimal lower bound on the communication complexity of gap hamming distance"
  Abstract: We prove an optimal $\Omega(n)$ lower bound on the randomized communication complexity of the much-studied Gap-Hamming-Distance problem. As a consequence, we obtain essentially optimal multi-pass space lower bounds in the data stream model for a number of fundamental problems, including the estimation of frequency moments. #R##N#The Gap-Hamming-Distance problem is a communication problem, wherein Alice and Bob receive $n$-bit strings $x$ and $y$, respectively. They are promised that the Hamming distance between $x$ and $y$ is either at least $n/2+\sqrt{n}$ or at most $n/2-\sqrt{n}$, and their goal is to decide which of these is the case. Since the formal presentation of the problem by Indyk and Woodruff (FOCS, 2003), it had been conjectured that the naive protocol, which uses $n$ bits of communication, is asymptotically optimal. The conjecture was shown to be true in several special cases, e.g., when the communication is deterministic, or when the number of rounds of communication is limited. #R##N#The proof of our aforementioned result, which settles this conjecture fully, is based on a new geometric statement regarding correlations in Gaussian space, related to a result of C. Borell (1985). To prove this geometric statement, we show that random projections of not-too-small sets in Gaussian space are close to a mixture of translated normal variables.

Connected through hyperedge HE-1 (induced by source paper "approximate mathbb f _2 sketching of valuation functions"):
  with members:
    - "optimality of linear sketching under modular updates"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "testing submodularity and other properties of valuation functions"
    - "almost optimal streaming algorithms for coverage problems"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "towards tight bounds for the streaming set cover problem"
    - "streaming algorithms for submodular function maximization"
    - "incidence geometries and the pass complexity of semi streaming set cover"

Connected through hyperedge HE-2 (induced by source paper "towards optimal moment estimation in streaming and distributed models"):
  with members:
    - "optimal random sampling from distributed streams revisited"
    - "weighted reservoir sampling from distributed streams"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "distributed statistical estimation of matrix products with applications"
    - "revisiting frequency moment estimation in random order streams"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "continuous monitoring of ell_p norms in data streams"
    - "communication optimal distributed clustering"

Connected through hyperedge HE-3 (induced by source paper "faster algorithms for high dimensional robust covariance estimation"):
  with members:
    - "high dimensional robust sparse regression"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "high dimensional robust mean estimation in nearly linear time"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "learning geometric concepts with nasty noise"
    - "being robust in high dimensions can be practical"
    - "robustly learning a gaussian getting optimal error efficiently"

Connected through hyperedge HE-4 (induced by source paper "the space complexity of inner product filters"):
  with members:
    - "norm ranging lsh for maximum inner product search"
    - "approximate nearest neighbors in limited space"
    - "adaptive mapreduce similarity joins"
    - "practical data dependent metric compression with provable guarantees"
    - "distributed pcp theorems for hardness of approximation in p"
    - "flexpoint an adaptive numerical format for efficient training of deep neural networks"
    - "bolt accelerated data mining with fast vector compression"
    - "scalable and robust set similarity join"

Connected through hyperedge HE-5 (induced by source paper "communication complexity of estimating correlations"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "strong data processing inequalities for channels and bayesian networks"
    - "secret key generation with limited interaction"
    - "smoothing brascamp lieb inequalities and strong converses for common randomness generation"
    - "secret key generation with one communicator and a one shot converse via hypercontractivity"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "converses for secret key agreement and secure computing"
    - "communication with imperfectly shared randomness"

Connected through hyperedge HE-6 (induced by source paper "l p pattern matching in a stream"):
  with members:
    - "approximating approximate pattern matching"
    - "the streaming k mismatch problem"
    - "approximate hamming distance in a stream"
    - "the k mismatch problem revisited"
    - "how to catch l 2 heavy hitters on sliding windows"

Connected through hyperedge HE-7 (induced by source paper "bare quantum simultaneity versus classical interactivity in communication complexity"):
  with members:
    - "quantum versus classical simultaneity in communication complexity"
    - "entangled simultaneity versus classical interactivity in communication complexity"

Connected through hyperedge HE-8 (induced by source paper "the abc problem and equator sampling renyi divergences"):
  with members:
    - "renyi divergence and kullback leibler divergence"

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)